Amazon Rekognition vs Tesseract OCR

Overview

Tesseract OCR

Stacks96

Followers286

Votes7

GitHub Stars70.7K

Forks10.4K

Amazon Rekognition

Stacks79

Followers152

Votes4

Amazon Rekognition vs Tesseract OCR: What are the differences?

Introduction

Amazon Rekognition and Tesseract OCR are two popular tools used for optical character recognition (OCR) tasks. Both have their own strengths and differences in terms of functionality and features. In this comparison, we will highlight the key differences between these two tools.

Accuracy and Confidence: One significant difference between Amazon Rekognition and Tesseract OCR lies in their accuracy and confidence levels. Amazon Rekognition, powered by advanced machine learning algorithms, tends to have higher accuracy rates and provides confidence scores for the recognized text. On the other hand, Tesseract OCR, while efficient, may have relatively lower accuracy, especially in cases with complex or distorted text.
Language Support: Another notable difference is the range of languages supported by each tool. Amazon Rekognition offers robust language support with a wide range of recognized languages, including regional dialects. It can accurately handle diverse text inputs from various languages. Tesseract OCR, although it supports multiple languages, may have limitations or inconsistencies in recognizing certain non-Latin characters or scripts.
Document Analysis and Layout: Amazon Rekognition goes beyond basic character recognition by offering document analysis and layout detection features. It can identify and extract information from structured documents like forms, invoices, or tables, providing valuable insights. Tesseract OCR, while excellent for text extraction, focuses primarily on character recognition and lacks advanced document analysis capabilities.
Cloud-based vs. On-premises: One significant difference lies in the deployment model. Amazon Rekognition is a cloud-based service, meaning it operates and stores data on remote servers. This enables scalability, accessibility, and eliminates the need for managing infrastructure. Tesseract OCR, however, is an open-source solution that needs to be installed and run locally, which gives users more control over their data but requires manual setup and maintenance.
Additional Image Analysis: Amazon Rekognition extends its functionality beyond OCR, offering additional image analysis capabilities. It can detect faces, objects, scenes, and perform visual search. This makes it useful for various image recognition tasks like image moderation, facial recognition, or content indexing. Tesseract OCR, being primarily focused on OCR, does not provide these advanced image analysis features.
Cost and Pricing Model: Lastly, the cost and pricing models differ between the two tools. Amazon Rekognition operates on a usage-based pricing model in the cloud, where you pay for the resources consumed and the features utilized. Tesseract OCR, being open-source, is free to use, but you need to manage the infrastructure and may have to invest in additional software or hardware components if required.

In summary, Amazon Rekognition offers higher accuracy, advanced document analysis, cloud-based deployment, additional image analysis capabilities, and a flexible pricing model, while Tesseract OCR provides a free, open-source solution, supports multiple languages, and allows for local control and customization.

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Advice on Tesseract OCR, 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 OCR	Amazon Rekognition
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.	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 70.7K	GitHub Stars -
GitHub Forks 10.4K	GitHub Forks -
Stacks 96	Stacks 79
Followers 286	Followers 152
Votes 7	Votes 4
Pros & Cons
Pros 5 Building training set is easy 2 Very lightweight library Cons 1 Works best with white background and black text	Pros 4 Integrate easily with AWS Cons 1 AWS

What are some alternatives to Tesseract OCR, 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.js

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.

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 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.

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.

libjpeg

It is a free library for JPEG image compression.

Related Comparisons

Amazon Rekognition vs Tesseract OCR: What are the differences?

Introduction

Accuracy and Confidence: One significant difference between Amazon Rekognition and Tesseract OCR lies in their accuracy and confidence levels. Amazon Rekognition, powered by advanced machine learning algorithms, tends to have higher accuracy rates and provides confidence scores for the recognized text. On the other hand, Tesseract OCR, while efficient, may have relatively lower accuracy, especially in cases with complex or distorted text.
Language Support: Another notable difference is the range of languages supported by each tool. Amazon Rekognition offers robust language support with a wide range of recognized languages, including regional dialects. It can accurately handle diverse text inputs from various languages. Tesseract OCR, although it supports multiple languages, may have limitations or inconsistencies in recognizing certain non-Latin characters or scripts.
Document Analysis and Layout: Amazon Rekognition goes beyond basic character recognition by offering document analysis and layout detection features. It can identify and extract information from structured documents like forms, invoices, or tables, providing valuable insights. Tesseract OCR, while excellent for text extraction, focuses primarily on character recognition and lacks advanced document analysis capabilities.
Cloud-based vs. On-premises: One significant difference lies in the deployment model. Amazon Rekognition is a cloud-based service, meaning it operates and stores data on remote servers. This enables scalability, accessibility, and eliminates the need for managing infrastructure. Tesseract OCR, however, is an open-source solution that needs to be installed and run locally, which gives users more control over their data but requires manual setup and maintenance.
Additional Image Analysis: Amazon Rekognition extends its functionality beyond OCR, offering additional image analysis capabilities. It can detect faces, objects, scenes, and perform visual search. This makes it useful for various image recognition tasks like image moderation, facial recognition, or content indexing. Tesseract OCR, being primarily focused on OCR, does not provide these advanced image analysis features.
Cost and Pricing Model: Lastly, the cost and pricing models differ between the two tools. Amazon Rekognition operates on a usage-based pricing model in the cloud, where you pay for the resources consumed and the features utilized. Tesseract OCR, being open-source, is free to use, but you need to manage the infrastructure and may have to invest in additional software or hardware components if required.

Amazon Rekognition vs Tesseract OCR

Overview

Amazon Rekognition vs Tesseract OCR: What are the differences?