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Datumbox Machine Learning Framework 0.7.0 Released

I am really excited to announce that, after several months of development, the new version of Datumbox is out! The 0.7.0 version brings multi-threading support, fast disk-based training for datasets that don’t fit in memory, several algorithmic enhancements and better architecture. Download it now from Github or Maven Central Repository. What is new? The focus […]

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I am really excited to announce that, after several months of development, the new version of Datumbox is out! The 0.7.0 version brings multi-threading support, fast disk-based training for datasets that don’t fit in memory, several algorithmic enhancements and better architecture. Download it now from Github or Maven Central Repository.

The focus of version 0.7.0 is to finally bring multi-threading support to the framework and make the disk-based training ultra fast. Moreover it brings several algorithmic enhancements in all the Regression-based algorithms, the Collaborative Filtering model and the N-grams extractor which is used in NLP applications. The architecture of the framework has been redesigned to separate the project into multiple modules (note that the artifactId of the main library is now datumbox-framework-lib) and to simplify its structure. Finally the new version brings several code improvements, better documentation in the form of javadocs and improved test coverage.

The 0.7.0 version of the framework is not backwards compatible with the 0.6.x branch. This is because major redevelopment was necessary in order to add the new features and improve & simplify the architecture of the framework. Below I discuss in detail the new features:

Multi-threading support

The new framework is several times faster than the 0.6.x branch. This was achieved by using threads, by doing heavy profiling on the hot spots of the code and by rewriting core components to enable non-blocking concurrent reads/writes. Currently threads are being used in all the algorithms that can be parallelized which is the majority of the supported models of the framework. The parallel execution is supported both during training and testing/predicting.

The project uses lots Java 8 features in order to reduce the verbosity of the code, improve readability and modernize the code-base. Note that even though the framework makes heavy use of streams, all tasks are executed in their own ForkJoinPool to ensure that they will not get stuck. The level of parallelism is controlled either by changing programmatically the ConcurrencyConfiguration object or by configuring the datumbox.config.properties file.

Disk-based Training

Even though disk-based training (training models without loading the data in memory) was possible since the 0.6.0 version, it was so slow that made the feature practically unusable. In version 0.7.0, the Storage Engine mechanism was redeveloped to enable a hybrid approach of storing the hot/regularly accessed records in memory & LRU cache while keeping the rest on disk. This approach makes the disk-based training very fast and it should be preferred even in cases where the data barely fit in memory (obviously if the data fit easily in RAM, the default in-memory training should be preferred). As in the previous version, the memory storage configuration can be changed programmatically by changing the appropriate DatabaseConfiguration objects or by configuring the datumbox.config.properties file.

At this point I would like to point out that this feature would not have been possible without the amazing work done by Jan Kotek on MapDB. MapDB is an embeded Java database engine which provides concurrent Maps backed by disk storage and off-heap-memory. Using his open-source library, I was able to develop a Storage Engine which enables Datumbox to handle several GB worth of training data on my laptop without loading them in memory.

Algorithmic Enhancements

The new version adds support of L1, L2 and ElasticNet regularization in the SoftMaxRegression (Multinomial Logistic Regresion), OrdinalRegression and NLMS (Linear Regression) models. This means that by using the same standard classes one can perform Ridge Regression, Lasso Regression or make use of Elastic Nets. Moreover in the new version the Collaborative Filtering algorithm was modified to support more generic User-user CF models. Finally the NgramsExtractor algorithm was rewritten to make it able to export more keywords and provide better scores.

Framework Architecture & Code Improvements

Another important update on the new framework is the fact that the project was split into multiple sub-modules. Below I list the currently supported modules named after their artifactIds:

  1. datumbox-framework-common: It contains the most important interfaces, helper and utility classes, data structures and mechanisms of the framework. This module does not contain any algorithms but it is the base of the framework.
  2. datumbox-framework-core: It consists of the 3 main layers of the framework (Machine Learning, Statistics and Mathematics) along with the utilities layer. This module contains all the algorithms, methods and statistical tests of the framework.
  3. datumbox-framework-applications: It contains a list of classes which are build to offer off-the-shelf solutions for common machine learning problems such as Text Classification, Data Modelling etc. All the classes of the module are built on top of the core module.
  4. datumbox-framework-lib: This is the Datumbox Machine Learning Framework! Note that the artifactId of the library changed from “datumbox-framework” to “datumbox-framework-lib” as a result of the restructuring.

In addition to the above modules, we have the “datumbox-framework” parent module which is no longer the Java library but simply groups together all the sub-modules under the same project. In order to use the new framework on Maven projects add in your pom.xml the following lines:

<dependencies> ... <dependency> <groupId>com.datumbox</groupId> <artifactId>datumbox-framework-lib</artifactId> <version>0.7.0</version> </dependency> ...
</dependencies>

The new version brings major changes on the structure of framework, the interfaces and inheritance with main goal to simplify and improve its architecture. One of the breaking changes that were introduced on the new framework is the deprecation of the old Dataset class (which was used to store all the training and testing data in the framework) and the introduction of the Dataframe class. The Dataframe class implements the Collection interface, allows the modification and deletion of records and enables the processing of the records in parallel. Another important change is the fact that the BaseMLrecommender, which is the base class for all Recommender System algorithms, now inherits from BaseMLmodel.

In addition to the above changes the framework includes some code enhancements and bug fixes: A serialVersionUID is added in every serializable class, the Exceptions and error messages have been improved and so do the javadocs documentation and the test-coverage. For more information about the updates of the new version have a look on the Changelog.

Datumbox 0.7.0 has completed several important milestones of the originally proposed roadmap. The development of the framework will continue in the following months to cover the following targets:

  1. Access the Framework via Console or Python: The framework should become more accessible to non-Java developers. To achieve this it should provide access to the algorithms via the command line or by offering an API in other languages like Python.
  2. New Machine Learning algorithms: As the architecture of the framework becomes more mature, it will be easier to increase the number of supported algorithms and include models such as Mixture of Gaussians, Gaussian Processes, k-NN, Decision Trees, Random Forests, Factor Analysis, SVD, Factorization Machines, Artificial Neural Networks etc.
  3. More Storage Engines: More options should be offered to the users of the framework to store their models and train their algorithms without loading all the data in memory. Moreover better tools should be provided to those who want to move a model from one storage engine to the other.
  4. Improve Documentation, Test coverage & Code examples: Even though the javadocs and test coverage improve in each release, the documentation of the framework is still poor. Next versions should provide a better documentation, better test-coverage and more examples on how to use the supported algorithms.

Given that I have a full-time job, I expect that the development of the framework will continue at the same rate, releasing a new version every 4-6 months. If you would like to propose a new milestone feel free to open an issue on the official Github repository. Last but not least, if you use the project please consider contributing. It does not matter if you are a ninja Java Developer, a rock-star Data Scientist or a power user of the library; I can use all the help I can get so feel free to get in touch with me.

Once again I would like to thank my friend and colleague Eleftherios Bampaletakis for helping me improve the architecture of the framework, his feedback was invaluable. Also I would like to thank Jan Kotek for offering free consulting on how to use efficiently MapDB and for open-sourcing such an amazing product. Moreover lots of thanks to ej-technologies GmbH and JetBrains for providing licenses for their amazing tools JProfiler and IntelliJ IDEA; they both offer amazing products that helped a lot the development of the framework. Last but not least, I’ll like to thank the love of my life, Kyriaki, for supporting and putting up with me while writing the project.

Don’t forget to clone the code of Datumbox v0.7.0 from Github. The library is available also on Maven Central Repository. Also have a look on the Detailed Installation Guide and on the Code Examples to find out more on how to use the framework.

I am looking forward to your comments and recommendations. Pull requests are always welcome! 🙂

About Vasilis Vryniotis

My name is Vasilis Vryniotis. I’m a Data Scientist, a Software Engineer, author of Datumbox Machine Learning Framework and a proud geek. Learn more

Source: http://blog.datumbox.com/datumbox-machine-learning-framework-0-7-0-released/

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5 Work From Home Office Essentials

Working remotely from home had been increasing in popularity, but it’s now become a necessity for many professionals due to the pandemic. “Some companies are eager to reopen their doors and return to the office, but a large number of employer and employees are making the transitional work environment a permanent change.”  They can’t guarantee […]

The post 5 Work From Home Office Essentials appeared first on Aiiot Talk – Artificial Intelligence | Internet of Things | Technology.

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Working remotely from home had been increasing in popularity, but it’s now become a necessity for many professionals due to the pandemic.

“Some companies are eager to reopen their doors and return to the office, but a large number of employer and employees are making the transitional work environment a permanent change.” 

They can’t guarantee their health and safety in a socially-crowded space, plus, companies are able to save tons of money they would have spent on their commercial lease or mortgage payments.

That’s not the say that working from home doesn’t come at its own costs, however. It can lead to a huge hit in productivity without the right equipment in place. To maximize your performance and efficiency in a remote setting, be sure to purchase these five office essentials.

1. Powerful PC

This one probably feels like an obvious pointer, but let’s knock it off our list. You won’t be able to get by with a make-shift work station and in today’s digital domain, your computer will be at the core of everything you do.

Never-ending loading wheels, delayed downloads, and slow rendering will add seconds to every task you do, so if your company didn’t provide you with a workhorse computer tower, you might look into investing in one yourself, then deduct the cost in your tax return.

Depending on your line of work, it might make more sense to go for a laptop vs a desktop computer. Unless your tasks demand super sophisticated software and large storage space, you can probably get by with a portable PC. That way, when coffee shops begin to reopen and allow patrons to sit inside, you can work on-the-go without feeling tethered to your desk.

2. Ergonomic Office Chair

If you’re looking at a long-term remote situation, it’s worth spending the big bucks on an ergonomic office chair. You should feel comfortably locked into your seat for eight hours a day—at least if you want to concentrate on your workflow, rather than the cramp in your back.

Shop around for an office chair that’s sophisticated in design and specifically built to hold the human body. Some stand-out features you should look out for include:

  • Targeted support around the lumbar spine
  • Adjustable height so you can adjust the seat as necessary for your arms to rest naturally on the keyboard
  • Swivel base to effortlessly turn your body, preventing neck strain
  • Cushioned seat to comfort your tailbone
  • Ventilated fabric that promotes airflow so you don’t feel overheated when sitting in the chair for several hours

You might have to pay a couple of hundred dollars for the best-of-the-line features, but there is another item that might qualify as an eligible tax deduction—just be sure to keep all your receipts organized with a document scanner in case the IRS raises their eyebrows and issues an audit.

3. Wireless Keyboard

If you want to type faster and feel better while you’re at it, then a wireless keyboard is clutch. They enable you to bring the keys closer, decreasing the extension length of your arms and accompanying shoulder strain.

“It also helps reduce the strain on your eyes by moving the bright screen farther away from your direct line of sight.” 

And, last but not least, the keys are placed in an ergonomic position for a more natural finger splay, with ample cushioning wrist cushioning that helps prevent overuse injuries such as a carpal tunnel.

4. Noise-cancelling Headphones

To truly get in the zone, you should block out distractions with headphones the cancel noise in your environment—especially if your work station is set up in a common area. Other tips to stay focused include installing a website blocker and leaving your cellphone on the other side of the room.

5. House plant or flowers

People are scientifically proven to be more productive when working near fresh flowers or lush greenery. The good news is that you don’t need to have a green thumb or natural lighting to achieve this effect—even artificial foliage can brighten your mood and improve your performance.

Working from home sometimes can feel like you’re locked inside all day, so bringing the outside world inside your space can help ward off burnout.

Take these tips with you into 2021 and set yourself up for success in your new home office setting.

Source: https://www.aiiottalk.com/business/work-from-home-office-essentials/

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zomato digitizes menus using Amazon Textract and Amazon SageMaker

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

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This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India.

Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing else will hit the spot! To help picky eaters satisfy their cravings, we at zomato have recently added enhanced search engine capabilities to our restaurant aggregation and food delivery platform. These capabilities enable us to recommend restaurants to zomato users based on searches for specific dishes.

We power this functionality with machine learning (ML), using it to extract and structure text data from menu images. To develop this menu digitization technology, we partnered with Amazon ML Solutions Lab to explore the capabilities of the AWS ML Stack. This post summarizes how we used Amazon Textract and Amazon SageMaker to develop a customized menu digitization solution.

Extracting raw text from menus with Amazon Textract

The first component of this solution was to accurately extract all the text in the menu image. This process is known as optical character recognition (OCR). For our use case, we experimented with both in-house and commercial OCR solutions.

We first created an in-house OCR solution by stacking a pre-trained text detection model and a pre-trained text recognition model. The challenge with these models was that they were trained on a standard text dataset that didn’t match the eclectic fonts found in restaurant menus. To improve system performance, we fine-tuned these models by generating a dataset of 1.5 million synthetic text images that were more representative of text in menus.

After evaluating our in-house solution and several commercial OCR solutions, we found that Amazon Textract offers the best text recognition precision and recall. Restaurants often get creative when designing their menus, so OCR robustness was crucial for this use case. Amazon Textract particularly differentiated itself when processing menus with unique fonts, background images, and low image resolutions. Using it is as simple as making an API call:

#Python 3.6
import boto3
textract_client = boto3.client( 'textract', region_name = '' #insert the AWS region you're working in
)
textract_response = textract_client.detect_document_text( Document={ 'S3Object': { 'Bucket': '', #insert the name of the S3 bucket containing your image 'Name': '' #insert the S3 key of your image } }
) print(textract_response)

The following code is the Amazon Textract output for a sample image:

{'DocumentMetadata': {'Pages': 1}, 'Blocks': [{'BlockType': 'PAGE', 'Geometry': {'BoundingBox': {'Width': 1.0, 'Height': 1.0, 'Left': 0.0, 'Top': 0.0}, ... {'BlockType': 'WORD', 'Text': 'Dim', 'Geometry': {'BoundingBox': {'Width': 0.10242128372192383, 'Height': 0. 048968635499477386, 'Left': 0. 24052166938781738, 'Top': 0. 02556285448372364},
... 

The raw outputs are visualized by overlaying them on top of the image. The following image visualizes the preceding raw output. The black boxes are the text-detection bounding boxes provided by Amazon Textract. Extracted text is displayed on the right. Note the unconventional fonts, colors, and images on this menu.

The following image visualizes Amazon Textract outputs for a menu with a different design. Black boxes are the text-detection bounding boxes provided by Amazon Textract. Extracted text is displayed on the right. Again, this menu has unconventional fonts, colors, and images.

Using Amazon SageMaker to build a menu structure detector

The next component of this solution was to group the detections from Amazon Textract by menu section. This enabled our search engine to distinguish between entrees, desserts, beverages, and so on. We framed this as a computer vision problem—object detection, to be precise—and used Amazon SageMaker Ground Truth to collect training data. Ground Truth accelerated this process by providing a fully managed annotation tool that we customized to ask human annotators to draw bounding boxes around every menu section in the image. We used an annotation workforce from AWS Marketplace because this was a niche labeling task, and public labelers from Amazon Mechanical Turk didn’t perform well. With Ground Truth, it took just a few days and approximately $1,400 to label 4,086 images with triplicate redundancy.

With labeled data in hand, we faced a paradox of choice when selecting model-building approaches because object detection is such a thoroughly studied problem. Our choices included:

  • Removing low-confidence labels from the labeled dataset – Because even human annotators can make mistakes, Ground Truth calculates confidence scores for labels by having multiple annotators (for this use case, three) label the same image. Setting a higher confidence threshold for labels can decrease the noise in the training data at the expense of having less training data.
  • Data augmentation – Techniques for image data augmentation include horizontal flipping, cropping, shearing, and rotation. Data augmentation can make models more robust by increasing the amount of training data. However, excessive data augmentation may result in poor model convergence.
  • Feature engineering – From our experience in applying computer vision to processing menus, we had a variety of techniques in mind to emphasize or de-emphasize various aspects of the input images. For example, see the following images.

The following is the original image of a menu.

The following image shows the redacted image (overlay white boxes on a black background where text detections were found).

The following is a text cropped image. On a black background, the image has overlay crops from the original image where text detections were found.

The following is a single channel and text cropped image. The image is encoded as a single RGB channel (for this image, green). You can apply this with other transformations, in this case text cropping.

 

We also had the following additional model-building methods to choose from:

  • Model architectures like YOLO, SSD, and RCNN, with VGG or ResNet backbones – Each architecture has different trade-offs of model accuracy, inference time, model size, and more. For this use case, model accuracy was the most important metric because menu images were batch processed.
  • Using a model pre-trained on a general object detection task or starting from scratch – Transfer learning can be helpful when training complex models on small datasets. However, the task of detecting menu sections is very different from a general object detection task (for example, PASCAL VOC), so the pre-training may not be relevant.
  • Optimizer parameters – These include learning rate, momentum, regularization coefficients, and early stopping configuration.

With so many hyperparameters to consider, we turned to the automatic tuning feature of Amazon SageMaker to coordinate a massive tuning job across all these variables. The following code is an example of tuning a single model architecture and input data configuration:

import sagemaker
import boto3
from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker.estimator import Estimator
from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter
import itertools
from time import sleep #set to the region you're working in
REGION_NAME = ''
#set a S3 path for SageMaker to store the outputs of the training jobs S3_OUTPUT_PATH = ''
#set a S3 location for your training dataset, #assumed to be an augmented manifest file
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/augmented-manifest.html
TRAIN_DATA_LOCATION = ''
#set a S3 location for your validation data, #assumed to be an augmented manifest file
VAL_DATA_LOCATION = ''
#specify which fields in the augmented manifest file are relevant for training
DATA_ATTRIBUTE_NAMES = [,]
#specify image shape
IMAGE_SHAPE = #specify label width
LABEL_WIDTH = #specify number of samples in the training dataset
NUM_TRAINING_SAMPLES = sgm_role = sagemaker.get_execution_role()
boto_session = boto3.session.Session( region_name = REGION_NAME
)
sgm_session = sagemaker.Session( boto_session = boto_session
)
training_image = get_image_uri( region_name = REGION_NAME, repo_name = 'object-detection', repo_version = 'latest'
) #set training job configuration
object_detection_estimator = Estimator( image_name = training_image, role = sgm_role, train_instance_count = 1, train_instance_type = 'ml.p3.2xlarge', train_volume_size = 50, train_max_run = 360000, input_mode = 'Pipe', output_path = S3_OUTPUT_PATH, sagemaker_session = sgm_session
) #set input data configuration
train_data = sagemaker.session.s3_input( s3_data = TRAIN_DATA_LOCATION, distribution = 'FullyReplicated', record_wrapping = 'RecordIO', s3_data_type = 'AugmentedManifestFile', attribute_names = DATA_ATTRIBUTE_NAMES
) val_data = sagemaker.session.s3_input( s3_data = VAL_DATA_LOCATION, distribution = 'FullyReplicated', record_wrapping = 'RecordIO', s3_data_type = 'AugmentedManifestFile', attribute_names = DATA_ATTRIBUTE_NAMES
) data_channels = { 'train': train_data, 'validation' : val_data
} #set static hyperparameters
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection-api-config.html
static_hyperparameters = { 'num_classes' : 1, 'epochs' : 100, 'lr_scheduler_step' : '15,30', 'lr_scheduler_factor' : 0.1, 'overlap_threshold' : 0.5, 'nms_threshold' : 0.45, 'image_shape' : IMAGE_SHAPE, 'label_width' : LABEL_WIDTH, 'num_training_samples' : NUM_TRAINING_SAMPLES, 'early_stopping' : True, 'early_stopping_min_epochs' : 5, 'early_stopping_patience' : 1, 'early_stopping_tolerance' : 0.05,
} #set ranges for tunable hyperparameters
hyperparameter_ranges = { 'learning_rate': ContinuousParameter( min_value = 1e-5, max_value = 1e-2, scaling_type = 'Auto' ), 'mini_batch_size': IntegerParameter( min_value = 8, max_value = 64, scaling_type = 'Auto' )
} #Not all hyperparameters are feasible to tune directly
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection-tuning.html
#For these we run model tuning jobs in parallel using a for loop
#We take this approach for tuning over different model architectures #and different feature engineering configurations
use_pretrained_options = [0, 1]
base_network_options = ['resnet-50', 'vgg-16'] for use_pretrained, base_network in itertools.product(use_pretrained_options, base_network_options): static_hyperparameter_configuration = { **static_hyperparameters, 'use_pretrained_model' : use_pretrained, 'base_network' : base_network } object_detection_estimator.set_hyperparameters( **static_hyperparameter_configuration ) tuner = HyperparameterTuner( estimator = object_detection_estimator, objective_metric_name = 'validation:mAP', strategy = 'Bayesian', hyperparameter_ranges = hyperparameter_ranges, max_jobs = 24, max_parallel_jobs = 2, early_stopping_type = 'Auto', ) tuner.fit( inputs = data_channels ) print(f'Started tuning job: {tuner.latest_tuning_job.name}') #wait a bit before starting next job so auto generated names don't conflict sleep(60)

This code uses version 1.72.0 of the Amazon SageMaker Python SDK, which is the default version installed in Amazon SageMaker notebook instances. Version 2.X introduces breaking changes. For more information, see Use Version 2.x of the SageMaker Python SDK.

We used powerful GPU hardware (p3.2xlarge instances), and it took us just 1 week and approximately $1,500 to explore 455 unique parameter configurations. Of these configurations, Amazon SageMaker found that a fine-tuned Faster R-CNN model with text cropping performed the best, with a mean average precision score of 0.93. This aligned with results from our prior work in this space, which found that two-stage detectors generally outperform single-stage detectors in processing menus.

The following is an example of how the object detection model processed a menu. In this image, the purple boxes are the predicted bounding boxes from the menu section detection model. Black boxes are the text detection bounding boxes provided by Amazon Textract.

Using Amazon SageMaker to build rule- and ML-based text classifiers

The final component in the solution was a layer of text classification. To enable our enhanced search functionality, we had to know if each detection within a menu section was the menu section title, name of a dish, price of a dish, or something else (such as a description of a dish or the name of the restaurant). To this end, we developed a hybrid rule- and ML-based text classification system.

The first step of the classification was to use a rule to determine if a detection was a price or not. This rule simply calculated the proportion of numeric characters in the detection. If the proportion was greater than 40%, the detection was classified as a price. Although simple, this classifier worked well in practice. We used Amazon SageMaker notebook instances as a convenient interactive environment to develop this and other rules.

After the prices were filtered out, the remaining detections were classified as dish or not dish. From our experience in processing menus, we intuitively knew that in many cases, the location of prices was sufficient to do this classification. For these menus, dishes and prices are listed side by side, so simply classifying detections located to the left of prices as dishes worked well.

The following example shows how the rules-based text classification system processed a menu. Green boxes are detections classified as dishes (by the price location rule). Red boxes are detections classified as not dishes (by the price location rule). Blue boxes are detections classified as prices. Final dish detections are on the right.

Some menus might include lengthy dish descriptions or may not list prices next to individual dishes. These menus violate the assumptions of the price location rules, so we turned to model-based text classification. We used Amazon SageMaker training jobs to experiment with many modeling approaches in parallel, including an XGBoost model trained on hashed word count vectors. In the end, we found that a fine-tuned BERT model from GluonNLP achieved the best performance with an AUROC score of 0.86.

The following image is an example of how the model-based text classification system processed a menu. Green boxes are detections classified as dishes (by the BERT model). Red boxes are detections classified as not dishes (by the BERT model). Blue boxes are detections classified as prices. The final dish detections are on the right.

Of the remaining detections (those not classified as prices or dishes), a final round of classification identified menu section titles. We created features that captured the font size of the detection, the location of the detection on the menu, and the length of the words within the detection. We used these features as inputs to a logistic regression model that predicted if a detection is a menu section title or not.

Key features of Amazon SageMaker

In the end, we found that doing OCR was as simple as making an API call to Amazon Textract. However, our use case required additional customization. We selected Amazon SageMaker as an ML platform to develop this customization because it offered several key features:

  • Amazon SageMaker Notebooks made it easy to spin up Jupyter notebook environments for prototyping and testing rules and models.
  • Ground Truth helped us build and deploy a custom image annotation tool with no front-end experience required.
  • Amazon SageMaker automatic tuning enabled us to run massive hyperparameter tuning jobs on powerful hardware, and included an intuitive interface for tracking the results of hundreds of experiments. You can implement tuning jobs with early stopping conditions, which makes experimentation cost-effective.

Amazon SageMaker offers additional integration benefits from including all the preceding features in a single platform:

  • Amazon SageMaker Notebooks come pre-installed with all the dependencies needed to build models that can be optimized with automatic tuning.
  • Ground Truth offers easy access to labelers from Mechanical Turk or AWS Marketplace.
  • Automatic tuning can directly ingest the manifest files created by Amazon SageMaker Ground Truth.

Putting it all together

Our menu digitization system can extract text from images of menus, group it by menu section, extract the title of the section, extract the dishes within each section, and pair each dish with its price. The following is a visualization of the end-to-end solution.

The workflow contains the following steps:

  1. The input is an image of a menu.
  2. Amazon Textract performs OCR on the input image.
  3. An ML-based computer vision model predicts bounding boxes for menu sections in the menu image.
  4. A rules-based classifier classifies Amazon Textract detections as price or not price.
  5. A rules-based classifier (5a) attempts to use the location of price detections to classify the not price detections as dish or not dish. If this rule doesn’t successfully classify most of the detections on the page, an ML-based classifier is used instead (5b).
  6. The ML-based classifier uses hand-crafted features to classify not dish detections as menu section title or not menu section title.
  7.  The menu text is structured by combining the menu section detections and the text classification results.

The following image visualizes a sample output of the system. Green boxes are detections classified as dishes. Blue boxes are detections classified as prices. Yellow boxes are detections classified as menu section titles. Purple boxes are predicted menu section bounding boxes.

The following code is the structured output:

[ { "title":{ "text":"Shrimp Dishes" }, "dishes":[ { "text":"Shrimp Masala", "price":{ "text":"140" } }, { "text":"Shrimp Biryani", "price":{ "text":"170" } }, { "text":"Shrimp Pulav", "price":{ "text":"160" } } ] }, ...
]

Conclusion

We built a system that uses ML to digitize menus without any human input required. This system will improve user experience by powering new features such as advanced dish search and review highlight verification. Our content team will also use it to accelerate creating menus for online ordering.

To explore these capabilities of Amazon Textract and Amazon SageMaker in more depth, see Automatically extract text and structured data from documents with Amazon Textract and Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning.

The Amazon ML Solutions Lab helped us accelerate our use of ML by pairing our team with ML experts. The ML Solutions Lab brings to every customer engagement learnings from more than 20 years of Amazon’s ML innovations in areas such as fulfillment and logistics, personalization and recommendations, computer vision and translation, fraud prevention, forecasting, and supply chain optimization. To learn more about the AWS ML Solutions Lab, contact your account manager or visit Amazon Machine Learning Solutions Lab.


About the Authors

Chiranjeev Ghai is a Machine Learning Engineer. In his current role, he has been aiding automation at zomato by leveraging a wide variety of ML optimisations ranging from Image Classification, Product Recommendation, and Text Detection. When not building models, he likes to spend his time playing video games at home.

Ryan Cheng is a Deep Learning Architect in the Amazon ML Solutions Lab. He has worked on a wide range of ML use cases from sports analytics to optical character recognition. In his spare time, Ryan enjoys cooking.

Andrew Ang is a Deep Learning Architect at the Amazon ML Solutions Lab, where he helps AWS customers identify and build AI/ML solutions to address their business problems.

Vinayak Arannil is a Data Scientist at the Amazon Machine Learning Solutions Lab. He has worked on various domains of data science like computer vision, natural language processing, recommendation systems, etc.

Source: https://aws.amazon.com/blogs/machine-learning/zomato-digitizes-menus-using-amazon-textract-and-amazon-sagemaker/

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zomato digitizes menus using Amazon Textract and Amazon SageMaker

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

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This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India.

Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing else will hit the spot! To help picky eaters satisfy their cravings, we at zomato have recently added enhanced search engine capabilities to our restaurant aggregation and food delivery platform. These capabilities enable us to recommend restaurants to zomato users based on searches for specific dishes.

We power this functionality with machine learning (ML), using it to extract and structure text data from menu images. To develop this menu digitization technology, we partnered with Amazon ML Solutions Lab to explore the capabilities of the AWS ML Stack. This post summarizes how we used Amazon Textract and Amazon SageMaker to develop a customized menu digitization solution.

Extracting raw text from menus with Amazon Textract

The first component of this solution was to accurately extract all the text in the menu image. This process is known as optical character recognition (OCR). For our use case, we experimented with both in-house and commercial OCR solutions.

We first created an in-house OCR solution by stacking a pre-trained text detection model and a pre-trained text recognition model. The challenge with these models was that they were trained on a standard text dataset that didn’t match the eclectic fonts found in restaurant menus. To improve system performance, we fine-tuned these models by generating a dataset of 1.5 million synthetic text images that were more representative of text in menus.

After evaluating our in-house solution and several commercial OCR solutions, we found that Amazon Textract offers the best text recognition precision and recall. Restaurants often get creative when designing their menus, so OCR robustness was crucial for this use case. Amazon Textract particularly differentiated itself when processing menus with unique fonts, background images, and low image resolutions. Using it is as simple as making an API call:

#Python 3.6
import boto3
textract_client = boto3.client( 'textract', region_name = '' #insert the AWS region you're working in
)
textract_response = textract_client.detect_document_text( Document={ 'S3Object': { 'Bucket': '', #insert the name of the S3 bucket containing your image 'Name': '' #insert the S3 key of your image } }
) print(textract_response)

The following code is the Amazon Textract output for a sample image:

{'DocumentMetadata': {'Pages': 1}, 'Blocks': [{'BlockType': 'PAGE', 'Geometry': {'BoundingBox': {'Width': 1.0, 'Height': 1.0, 'Left': 0.0, 'Top': 0.0}, ... {'BlockType': 'WORD', 'Text': 'Dim', 'Geometry': {'BoundingBox': {'Width': 0.10242128372192383, 'Height': 0. 048968635499477386, 'Left': 0. 24052166938781738, 'Top': 0. 02556285448372364},
... 

The raw outputs are visualized by overlaying them on top of the image. The following image visualizes the preceding raw output. The black boxes are the text-detection bounding boxes provided by Amazon Textract. Extracted text is displayed on the right. Note the unconventional fonts, colors, and images on this menu.

The following image visualizes Amazon Textract outputs for a menu with a different design. Black boxes are the text-detection bounding boxes provided by Amazon Textract. Extracted text is displayed on the right. Again, this menu has unconventional fonts, colors, and images.

Using Amazon SageMaker to build a menu structure detector

The next component of this solution was to group the detections from Amazon Textract by menu section. This enabled our search engine to distinguish between entrees, desserts, beverages, and so on. We framed this as a computer vision problem—object detection, to be precise—and used Amazon SageMaker Ground Truth to collect training data. Ground Truth accelerated this process by providing a fully managed annotation tool that we customized to ask human annotators to draw bounding boxes around every menu section in the image. We used an annotation workforce from AWS Marketplace because this was a niche labeling task, and public labelers from Amazon Mechanical Turk didn’t perform well. With Ground Truth, it took just a few days and approximately $1,400 to label 4,086 images with triplicate redundancy.

With labeled data in hand, we faced a paradox of choice when selecting model-building approaches because object detection is such a thoroughly studied problem. Our choices included:

  • Removing low-confidence labels from the labeled dataset – Because even human annotators can make mistakes, Ground Truth calculates confidence scores for labels by having multiple annotators (for this use case, three) label the same image. Setting a higher confidence threshold for labels can decrease the noise in the training data at the expense of having less training data.
  • Data augmentation – Techniques for image data augmentation include horizontal flipping, cropping, shearing, and rotation. Data augmentation can make models more robust by increasing the amount of training data. However, excessive data augmentation may result in poor model convergence.
  • Feature engineering – From our experience in applying computer vision to processing menus, we had a variety of techniques in mind to emphasize or de-emphasize various aspects of the input images. For example, see the following images.

The following is the original image of a menu.

The following image shows the redacted image (overlay white boxes on a black background where text detections were found).

The following is a text cropped image. On a black background, the image has overlay crops from the original image where text detections were found.

The following is a single channel and text cropped image. The image is encoded as a single RGB channel (for this image, green). You can apply this with other transformations, in this case text cropping.

 

We also had the following additional model-building methods to choose from:

  • Model architectures like YOLO, SSD, and RCNN, with VGG or ResNet backbones – Each architecture has different trade-offs of model accuracy, inference time, model size, and more. For this use case, model accuracy was the most important metric because menu images were batch processed.
  • Using a model pre-trained on a general object detection task or starting from scratch – Transfer learning can be helpful when training complex models on small datasets. However, the task of detecting menu sections is very different from a general object detection task (for example, PASCAL VOC), so the pre-training may not be relevant.
  • Optimizer parameters – These include learning rate, momentum, regularization coefficients, and early stopping configuration.

With so many hyperparameters to consider, we turned to the automatic tuning feature of Amazon SageMaker to coordinate a massive tuning job across all these variables. The following code is an example of tuning a single model architecture and input data configuration:

import sagemaker
import boto3
from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker.estimator import Estimator
from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter
import itertools
from time import sleep #set to the region you're working in
REGION_NAME = ''
#set a S3 path for SageMaker to store the outputs of the training jobs S3_OUTPUT_PATH = ''
#set a S3 location for your training dataset, #assumed to be an augmented manifest file
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/augmented-manifest.html
TRAIN_DATA_LOCATION = ''
#set a S3 location for your validation data, #assumed to be an augmented manifest file
VAL_DATA_LOCATION = ''
#specify which fields in the augmented manifest file are relevant for training
DATA_ATTRIBUTE_NAMES = [,]
#specify image shape
IMAGE_SHAPE = #specify label width
LABEL_WIDTH = #specify number of samples in the training dataset
NUM_TRAINING_SAMPLES = sgm_role = sagemaker.get_execution_role()
boto_session = boto3.session.Session( region_name = REGION_NAME
)
sgm_session = sagemaker.Session( boto_session = boto_session
)
training_image = get_image_uri( region_name = REGION_NAME, repo_name = 'object-detection', repo_version = 'latest'
) #set training job configuration
object_detection_estimator = Estimator( image_name = training_image, role = sgm_role, train_instance_count = 1, train_instance_type = 'ml.p3.2xlarge', train_volume_size = 50, train_max_run = 360000, input_mode = 'Pipe', output_path = S3_OUTPUT_PATH, sagemaker_session = sgm_session
) #set input data configuration
train_data = sagemaker.session.s3_input( s3_data = TRAIN_DATA_LOCATION, distribution = 'FullyReplicated', record_wrapping = 'RecordIO', s3_data_type = 'AugmentedManifestFile', attribute_names = DATA_ATTRIBUTE_NAMES
) val_data = sagemaker.session.s3_input( s3_data = VAL_DATA_LOCATION, distribution = 'FullyReplicated', record_wrapping = 'RecordIO', s3_data_type = 'AugmentedManifestFile', attribute_names = DATA_ATTRIBUTE_NAMES
) data_channels = { 'train': train_data, 'validation' : val_data
} #set static hyperparameters
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection-api-config.html
static_hyperparameters = { 'num_classes' : 1, 'epochs' : 100, 'lr_scheduler_step' : '15,30', 'lr_scheduler_factor' : 0.1, 'overlap_threshold' : 0.5, 'nms_threshold' : 0.45, 'image_shape' : IMAGE_SHAPE, 'label_width' : LABEL_WIDTH, 'num_training_samples' : NUM_TRAINING_SAMPLES, 'early_stopping' : True, 'early_stopping_min_epochs' : 5, 'early_stopping_patience' : 1, 'early_stopping_tolerance' : 0.05,
} #set ranges for tunable hyperparameters
hyperparameter_ranges = { 'learning_rate': ContinuousParameter( min_value = 1e-5, max_value = 1e-2, scaling_type = 'Auto' ), 'mini_batch_size': IntegerParameter( min_value = 8, max_value = 64, scaling_type = 'Auto' )
} #Not all hyperparameters are feasible to tune directly
#see: https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection-tuning.html
#For these we run model tuning jobs in parallel using a for loop
#We take this approach for tuning over different model architectures #and different feature engineering configurations
use_pretrained_options = [0, 1]
base_network_options = ['resnet-50', 'vgg-16'] for use_pretrained, base_network in itertools.product(use_pretrained_options, base_network_options): static_hyperparameter_configuration = { **static_hyperparameters, 'use_pretrained_model' : use_pretrained, 'base_network' : base_network } object_detection_estimator.set_hyperparameters( **static_hyperparameter_configuration ) tuner = HyperparameterTuner( estimator = object_detection_estimator, objective_metric_name = 'validation:mAP', strategy = 'Bayesian', hyperparameter_ranges = hyperparameter_ranges, max_jobs = 24, max_parallel_jobs = 2, early_stopping_type = 'Auto', ) tuner.fit( inputs = data_channels ) print(f'Started tuning job: {tuner.latest_tuning_job.name}') #wait a bit before starting next job so auto generated names don't conflict sleep(60)

This code uses version 1.72.0 of the Amazon SageMaker Python SDK, which is the default version installed in Amazon SageMaker notebook instances. Version 2.X introduces breaking changes. For more information, see Use Version 2.x of the SageMaker Python SDK.

We used powerful GPU hardware (p3.2xlarge instances), and it took us just 1 week and approximately $1,500 to explore 455 unique parameter configurations. Of these configurations, Amazon SageMaker found that a fine-tuned Faster R-CNN model with text cropping performed the best, with a mean average precision score of 0.93. This aligned with results from our prior work in this space, which found that two-stage detectors generally outperform single-stage detectors in processing menus.

The following is an example of how the object detection model processed a menu. In this image, the purple boxes are the predicted bounding boxes from the menu section detection model. Black boxes are the text detection bounding boxes provided by Amazon Textract.

Using Amazon SageMaker to build rule- and ML-based text classifiers

The final component in the solution was a layer of text classification. To enable our enhanced search functionality, we had to know if each detection within a menu section was the menu section title, name of a dish, price of a dish, or something else (such as a description of a dish or the name of the restaurant). To this end, we developed a hybrid rule- and ML-based text classification system.

The first step of the classification was to use a rule to determine if a detection was a price or not. This rule simply calculated the proportion of numeric characters in the detection. If the proportion was greater than 40%, the detection was classified as a price. Although simple, this classifier worked well in practice. We used Amazon SageMaker notebook instances as a convenient interactive environment to develop this and other rules.

After the prices were filtered out, the remaining detections were classified as dish or not dish. From our experience in processing menus, we intuitively knew that in many cases, the location of prices was sufficient to do this classification. For these menus, dishes and prices are listed side by side, so simply classifying detections located to the left of prices as dishes worked well.

The following example shows how the rules-based text classification system processed a menu. Green boxes are detections classified as dishes (by the price location rule). Red boxes are detections classified as not dishes (by the price location rule). Blue boxes are detections classified as prices. Final dish detections are on the right.

Some menus might include lengthy dish descriptions or may not list prices next to individual dishes. These menus violate the assumptions of the price location rules, so we turned to model-based text classification. We used Amazon SageMaker training jobs to experiment with many modeling approaches in parallel, including an XGBoost model trained on hashed word count vectors. In the end, we found that a fine-tuned BERT model from GluonNLP achieved the best performance with an AUROC score of 0.86.

The following image is an example of how the model-based text classification system processed a menu. Green boxes are detections classified as dishes (by the BERT model). Red boxes are detections classified as not dishes (by the BERT model). Blue boxes are detections classified as prices. The final dish detections are on the right.

Of the remaining detections (those not classified as prices or dishes), a final round of classification identified menu section titles. We created features that captured the font size of the detection, the location of the detection on the menu, and the length of the words within the detection. We used these features as inputs to a logistic regression model that predicted if a detection is a menu section title or not.

Key features of Amazon SageMaker

In the end, we found that doing OCR was as simple as making an API call to Amazon Textract. However, our use case required additional customization. We selected Amazon SageMaker as an ML platform to develop this customization because it offered several key features:

  • Amazon SageMaker Notebooks made it easy to spin up Jupyter notebook environments for prototyping and testing rules and models.
  • Ground Truth helped us build and deploy a custom image annotation tool with no front-end experience required.
  • Amazon SageMaker automatic tuning enabled us to run massive hyperparameter tuning jobs on powerful hardware, and included an intuitive interface for tracking the results of hundreds of experiments. You can implement tuning jobs with early stopping conditions, which makes experimentation cost-effective.

Amazon SageMaker offers additional integration benefits from including all the preceding features in a single platform:

  • Amazon SageMaker Notebooks come pre-installed with all the dependencies needed to build models that can be optimized with automatic tuning.
  • Ground Truth offers easy access to labelers from Mechanical Turk or AWS Marketplace.
  • Automatic tuning can directly ingest the manifest files created by Amazon SageMaker Ground Truth.

Putting it all together

Our menu digitization system can extract text from images of menus, group it by menu section, extract the title of the section, extract the dishes within each section, and pair each dish with its price. The following is a visualization of the end-to-end solution.

The workflow contains the following steps:

  1. The input is an image of a menu.
  2. Amazon Textract performs OCR on the input image.
  3. An ML-based computer vision model predicts bounding boxes for menu sections in the menu image.
  4. A rules-based classifier classifies Amazon Textract detections as price or not price.
  5. A rules-based classifier (5a) attempts to use the location of price detections to classify the not price detections as dish or not dish. If this rule doesn’t successfully classify most of the detections on the page, an ML-based classifier is used instead (5b).
  6. The ML-based classifier uses hand-crafted features to classify not dish detections as menu section title or not menu section title.
  7.  The menu text is structured by combining the menu section detections and the text classification results.

The following image visualizes a sample output of the system. Green boxes are detections classified as dishes. Blue boxes are detections classified as prices. Yellow boxes are detections classified as menu section titles. Purple boxes are predicted menu section bounding boxes.

The following code is the structured output:

[ { "title":{ "text":"Shrimp Dishes" }, "dishes":[ { "text":"Shrimp Masala", "price":{ "text":"140" } }, { "text":"Shrimp Biryani", "price":{ "text":"170" } }, { "text":"Shrimp Pulav", "price":{ "text":"160" } } ] }, ...
]

Conclusion

We built a system that uses ML to digitize menus without any human input required. This system will improve user experience by powering new features such as advanced dish search and review highlight verification. Our content team will also use it to accelerate creating menus for online ordering.

To explore these capabilities of Amazon Textract and Amazon SageMaker in more depth, see Automatically extract text and structured data from documents with Amazon Textract and Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning.

The Amazon ML Solutions Lab helped us accelerate our use of ML by pairing our team with ML experts. The ML Solutions Lab brings to every customer engagement learnings from more than 20 years of Amazon’s ML innovations in areas such as fulfillment and logistics, personalization and recommendations, computer vision and translation, fraud prevention, forecasting, and supply chain optimization. To learn more about the AWS ML Solutions Lab, contact your account manager or visit Amazon Machine Learning Solutions Lab.


About the Authors

Chiranjeev Ghai is a Machine Learning Engineer. In his current role, he has been aiding automation at zomato by leveraging a wide variety of ML optimisations ranging from Image Classification, Product Recommendation, and Text Detection. When not building models, he likes to spend his time playing video games at home.

Ryan Cheng is a Deep Learning Architect in the Amazon ML Solutions Lab. He has worked on a wide range of ML use cases from sports analytics to optical character recognition. In his spare time, Ryan enjoys cooking.

Andrew Ang is a Deep Learning Architect at the Amazon ML Solutions Lab, where he helps AWS customers identify and build AI/ML solutions to address their business problems.

Vinayak Arannil is a Data Scientist at the Amazon Machine Learning Solutions Lab. He has worked on various domains of data science like computer vision, natural language processing, recommendation systems, etc.

Source: https://aws.amazon.com/blogs/machine-learning/zomato-digitizes-menus-using-amazon-textract-and-amazon-sagemaker/

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