Overview

Google Cloud Vision API

Stacks134

Followers276

Votes16

Tesseract.js

Stacks37

Followers105

Votes2

GitHub Stars37.4K

Forks2.3K

Amazon Rekognition

Stacks80

Followers152

Votes4

Amazon Rekognition vs Google Cloud Vision API vs Tesseract.js: What are the differences?

Introduction:

Key differences between Amazon Rekognition, Google Cloud Vision API, and Tesseract.js:

Feature Set: Amazon Rekognition offers a wide range of features including facial analysis, object and scene detection, text in image recognition, and celebrity recognition. Google Cloud Vision API also provides similar features with the addition of label detection, landmark detection, and logo detection. On the other hand, Tesseract.js focuses mainly on optical character recognition (OCR) capabilities.
Scalability: Amazon Rekognition and Google Cloud Vision API are cloud-based services, providing scalability to handle large volumes of image data efficiently. They also offer integration with other cloud services for seamless workflows. Tesseract.js, on the other hand, is an open-source JavaScript library that runs locally, limiting its scalability compared to cloud-based solutions.
Accuracy and Performance: Amazon Rekognition and Google Cloud Vision API are backed by advanced machine learning algorithms and have higher accuracy rates in image recognition tasks. They also offer faster processing times due to their cloud infrastructure. While Tesseract.js is a capable OCR tool, its performance may vary depending on the quality of the images and the complexity of the text.
Cost: Amazon Rekognition and Google Cloud Vision API follow a pay-as-you-go pricing model based on the number of images processed or features used. The cost can vary depending on the scale of usage and additional services required. Tesseract.js, being an open-source library, is free to use but may require additional development resources for integration and maintenance.
Customization and Training: Amazon Rekognition and Google Cloud Vision API offer options for custom training models and fine-tuning algorithms for specific use cases. This enables users to improve accuracy and performance for specialized tasks. Tesseract.js, while capable of handling various languages and fonts, may require more manual tweaking to achieve similar levels of customization.
Integration and Ecosystem: Amazon Rekognition and Google Cloud Vision API have robust APIs and SDKs that allow seamless integration with various platforms and programming languages. They also have strong developer communities and support documentation. On the other hand, Tesseract.js, being a JavaScript library, is well-suited for web applications but may require additional plugins or frameworks for broader integrations.

In Summary, the key differences between Amazon Rekognition, Google Cloud Vision API, and Tesseract.js lie in their feature sets, scalability, accuracy, cost, customization options, and integration capabilities.

Advice on Google Cloud Vision API, 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.

53.3k views53.3k

Comments

Detailed Comparison

Google Cloud Vision API	Tesseract.js	Amazon Rekognition
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.	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.
Powerful Image Analysis; Insight From Your Images; Detect Inappropriate Content; Image Sentiment Analysis; Extract Text	-	-
Statistics
GitHub Stars -	GitHub Stars 37.4K	GitHub Stars -
GitHub Forks -	GitHub Forks 2.3K	GitHub Forks -
Stacks 134	Stacks 37	Stacks 80
Followers 276	Followers 105	Followers 152
Votes 16	Votes 2	Votes 4
Pros & Cons
Pros 9 Image Recognition 7 Built by Google	Pros 2 Graph Recognization	Pros 4 Integrate easily with AWS Cons 1 AWS

What are some alternatives to Google Cloud Vision API, Tesseract.js, Amazon Rekognition?

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.

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.

ZXing

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

OpenJPEG

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

EasyOCR

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

Restb.ai

It tags, classifies, and organizes your real estate images.

scanR

scanR is a simple OCR API service that supports 32 languages and can extract text from images or PDF files.

libjpeg

It is a free library for JPEG image compression.

MMOCR

It is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction.