Amazon Rekognition vs Tesseract.js

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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CLI (Node.js)

Manual

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?

Google Cloud Vision API

Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

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.

Free AI Image Detector

Is this image AI-generated? Free AI detector with 99.7% accuracy detects fake photos, deepfakes, and AI images from DALL-E, Midjourney, Stable Diffusion. No signup required.

Image to Prompt AI

Free AI-powered image to prompt generator. Upload images and get detailed prompts for AI art generation with our advanced converter.

Free Online Background Remover

BGRemoverFree is a smart AI tool designed to turn any image into a clean, professional visual within seconds. With a single upload, it automatically removes distracting backgrounds and highlights the main subject with perfect clarity. Whether you're preparing product photos, designing social media content, or creating marketing materials, BGRemoverFree gives you studio-quality cutouts without any editing skills. Fast, accurate, and fully web-based — it’s the easiest way to create polished, ready-to-use images for any purpose.

SAM 3D

Meta's SAM 3D brings human-level 3D perception to computer vision. Reconstruct objects and bodies from single images with unprecedented accuracy and speed.

libpng

It is the official Portable Network Graphics (PNG) reference library. It is a platform-independent library that contains C functions for handling PNG images. It supports almost all of PNG's features, is extensible, and has been widely used and tested.

OpenJPEG

It is an open-source JPEG 2000 codec written in C language.

ZXing

It is a barcode scanning library for Java, Android. Decode a 1D or 2D barcode from an image on the web.

EasyOCR

It is ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai.

Related Comparisons

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.

Amazon Rekognition vs Tesseract.js

Overview