Tesseract OCR vs scikit-image

Overview

scikit-image

Stacks311

Followers129

Votes12

GitHub Stars6.4K

Forks2.3K

Tesseract OCR

Stacks96

Followers286

Votes7

GitHub Stars70.7K

Forks10.4K

Tesseract OCR vs scikit-image: What are the differences?

1. Input requirements: Tesseract OCR requires the input images to be in the form of a bitmap or a pixmap, while scikit-image can work with various image formats such as JPEG, PNG, and TIFF. Additionally, Tesseract OCR requires the image to be preprocessed and converted to grayscale, while scikit-image can handle color images directly.
2. Accuracy and speed: Tesseract OCR is known for its high accuracy in recognizing text from images, especially for printed text. On the other hand, scikit-image provides a wider range of image processing capabilities but may not offer the same level of accuracy as Tesseract OCR for text recognition tasks. In terms of speed, scikit-image is generally faster in performing image processing operations compared to Tesseract OCR, which focuses more on accuracy.
3. Language support: Tesseract OCR supports a wide range of languages and can be easily trained for specific languages. This makes it a suitable choice for multilingual applications. On the contrary, scikit-image does not have built-in language support specifically for text recognition, as it is primarily focused on image processing tasks and does not have dedicated text recognition algorithms.
4. Text extraction and output: Tesseract OCR is specifically designed for extracting text from images and provides rich output options such as recognized text, confidence scores, and bounding box coordinates. In contrast, scikit-image does not have built-in functionality for text extraction and output. It primarily focuses on image processing operations and provides different output formats such as processed images or numerical arrays.
5. Integration and dependencies: Tesseract OCR is primarily a command-line tool and can be integrated into various programming languages through wrappers or APIs. It has relatively fewer dependencies compared to scikit-image, which is a Python library that relies on other libraries such as NumPy, SciPy, and Matplotlib. Therefore, scikit-image requires a more comprehensive installation process and may have additional dependencies to consider.
6. Community and documentation: Both Tesseract OCR and scikit-image have active communities and good documentation resources. However, Tesseract OCR has been widely adopted and extensively used in the field of optical character recognition, resulting in a larger community support and a vast amount of online resources and tutorials available.

In Summary, Tesseract OCR and scikit-image differ in terms of input requirements, accuracy and speed, language support, text extraction and output, integration and dependencies, as well as community and documentation resources.

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Advice on scikit-image, Tesseract OCR

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

scikit-image	Tesseract OCR
scikit-image is a collection of algorithms for image processing.	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.
Provides I/O, filtering, morphology, transformations, measurement, annotation, color conversions, test data sets, etc.;Written in Python with a well-commented source code;Has had 5,709 commits made by 116 contributors representing 29,953 lines of code;Released under BSD-3-Clause license	-
Statistics
GitHub Stars 6.4K	GitHub Stars 70.7K
GitHub Forks 2.3K	GitHub Forks 10.4K
Stacks 311	Stacks 96
Followers 129	Followers 286
Votes 12	Votes 7
Pros & Cons
Pros 6 More powerful 4 Anaconda compatibility 2 Great documentation	Pros 5 Building training set is easy 2 Very lightweight library Cons 1 Works best with white background and black text

What are some alternatives to scikit-image, Tesseract OCR?

Cloudinary

Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

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.

Cloudimage

Effortless image resizing, optimization and CDN delivery. Make your site fully responsive and really fast.

Kraken.io

It supports JPEG, PNG and GIF files. You can optimize your images in two ways - by providing an URL of the image you want to optimize or by uploading an image file directly to its API.

ImageEngine

ImageEngine is an intelligent Image CDN that dynamically optimizes image content tailored to the end users device. Using device intelligence at the CDN edge, developers can greatly simplify their image management process while accelerating their site.

FFMPEG

The universal multimedia toolkit.

Amazon Rekognition

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.

Related Comparisons

Tesseract OCR vs scikit-image: What are the differences?

1. Input requirements: Tesseract OCR requires the input images to be in the form of a bitmap or a pixmap, while scikit-image can work with various image formats such as JPEG, PNG, and TIFF. Additionally, Tesseract OCR requires the image to be preprocessed and converted to grayscale, while scikit-image can handle color images directly.
2. Accuracy and speed: Tesseract OCR is known for its high accuracy in recognizing text from images, especially for printed text. On the other hand, scikit-image provides a wider range of image processing capabilities but may not offer the same level of accuracy as Tesseract OCR for text recognition tasks. In terms of speed, scikit-image is generally faster in performing image processing operations compared to Tesseract OCR, which focuses more on accuracy.
3. Language support: Tesseract OCR supports a wide range of languages and can be easily trained for specific languages. This makes it a suitable choice for multilingual applications. On the contrary, scikit-image does not have built-in language support specifically for text recognition, as it is primarily focused on image processing tasks and does not have dedicated text recognition algorithms.
4. Text extraction and output: Tesseract OCR is specifically designed for extracting text from images and provides rich output options such as recognized text, confidence scores, and bounding box coordinates. In contrast, scikit-image does not have built-in functionality for text extraction and output. It primarily focuses on image processing operations and provides different output formats such as processed images or numerical arrays.
5. Integration and dependencies: Tesseract OCR is primarily a command-line tool and can be integrated into various programming languages through wrappers or APIs. It has relatively fewer dependencies compared to scikit-image, which is a Python library that relies on other libraries such as NumPy, SciPy, and Matplotlib. Therefore, scikit-image requires a more comprehensive installation process and may have additional dependencies to consider.
6. Community and documentation: Both Tesseract OCR and scikit-image have active communities and good documentation resources. However, Tesseract OCR has been widely adopted and extensively used in the field of optical character recognition, resulting in a larger community support and a vast amount of online resources and tutorials available.

Tesseract OCR vs scikit-image

Overview