Overview

Tesseract.js

Stacks41

Followers105

Votes2

GitHub Stars37.4K

Forks2.3K

Amazon Rekognition

Stacks79

Followers152

Votes4

Amazon Rekognition vs Tesseract.js: What are the differences?

Technology and Purpose: Amazon Rekognition uses deep learning technology for image analysis, providing facial recognition, object detection, and image recognition capabilities. On the other hand, Tesseract.js focuses on text extraction from images using OCR techniques, making it ideal for tasks like scanning documents or extracting text from photos.
Deployment and scalability: Amazon Rekognition is a managed service that offers scalable and cloud-based image analysis capabilities, making it suitable for enterprise-level applications with large amounts of data. In contrast, Tesseract.js runs entirely on the client-side, which limits its scalability and performance compared to Amazon Rekognition's cloud-based infrastructure.
Supported languages: Amazon Rekognition supports multiple programming languages through its SDKs, making it accessible for developers using different programming environments. Tesseract.js, being a JavaScript library, primarily targets web developers and can be integrated into web applications easily using JavaScript.
Accuracy and Performance: Amazon Rekognition is known for its high accuracy in image analysis tasks due to its sophisticated deep learning models and continuous training by Amazon. Tesseract.js, while capable of decent OCR performance, may not always match the accuracy levels of specialized image recognition services like Amazon Rekognition.
Cost and Pricing model: Amazon Rekognition follows a pay-as-you-go pricing model based on the number of image analyses performed, making it cost-effective for applications with varying demands. Tesseract.js, being open-source, is free to use, but it requires additional resources for deployment and maintenance, making it potentially more expensive in the long run for certain use cases.

In Summary, Amazon Rekognition and Tesseract.js differ in their technology focus, deployment models, scalability, language support, accuracy, and pricing strategies.

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Advice on Tesseract.js, Amazon Rekognition

Vladyslav

Sr. Directory of Technology at Shelf

Oct 25, 2019

Decided

AWS Rekognition has an OCR feature but can recognize only up to 50 words per image, which is a deal-breaker for us. (see my tweet).

Also, we discovered fantastic speed and quality improvements in the 4.x versions of Tesseract. Meanwhile, the quality of AWS Rekognition's OCR remains to be mediocre in comparison.

We run Tesseract serverlessly in AWS Lambda via aws-lambda-tesseract library that we made open-source.

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Detailed Comparison

Tesseract.js	Amazon Rekognition
This library supports over 60 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS.	Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
Statistics
GitHub Stars 37.4K	GitHub Stars -
GitHub Forks 2.3K	GitHub Forks -
Stacks 41	Stacks 79
Followers 105	Followers 152
Votes 2	Votes 4
Pros & Cons
Pros 2 Graph Recognization	Pros 4 Integrate easily with AWS Cons 1 AWS

What are some alternatives to Tesseract.js, Amazon Rekognition?

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Tesseract OCR

Tesseract was originally developed at Hewlett-Packard Laboratories Bristol and at Hewlett-Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. In 2005 Tesseract was open sourced by HP. Since 2006 it is developed by Google.

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libpng

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