TensorFlow vs Tesseract OCR

Overview

Tesseract OCR

Stacks96

Followers286

Votes7

GitHub Stars70.7K

Forks10.4K

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

TensorFlow vs Tesseract OCR: What are the differences?

Programming Language: TensorFlow is written in Python, while Tesseract OCR is written in C++.
Functionality: TensorFlow is primarily used for deep learning and machine learning tasks, such as building and training neural networks, while Tesseract OCR is specifically designed for optical character recognition (OCR).
Supported Platforms: TensorFlow is a cross-platform library that can run on various operating systems like Windows, macOS, and Linux, while Tesseract OCR is also cross-platform but can run on fewer systems, including Windows and Linux.
Image Processing: TensorFlow provides a wide range of image processing capabilities, including image recognition, segmentation, and transformation, while Tesseract OCR is focused solely on text extraction from images and does not offer extensive image processing functionality.
Model Training: TensorFlow offers a comprehensive framework for training machine learning models, including the ability to define and optimize the model architecture, while Tesseract OCR is designed to work with pre-trained models and does not provide built-in support for training custom models.
Accuracy and Recognition: TensorFlow has a wider range of applications and can achieve higher accuracy in various tasks beyond OCR, while Tesseract OCR focuses specifically on text recognition and extraction and is optimized for OCR tasks.

In Summary, TensorFlow is a versatile deep learning framework that supports a variety of tasks beyond OCR, while Tesseract OCR is a specialized OCR tool primarily focused on accurate text extraction from images.

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Advice on Tesseract OCR, TensorFlow

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

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

Tesseract OCR	TensorFlow
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.	TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Statistics
GitHub Stars 70.7K	GitHub Stars 192.3K
GitHub Forks 10.4K	GitHub Forks 74.9K
Stacks 96	Stacks 3.9K
Followers 286	Followers 3.5K
Votes 7	Votes 106
Pros & Cons
Pros 5 Building training set is easy 2 Very lightweight library Cons 1 Works best with white background and black text	Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful
Integrations
No integrations available	JavaScript

What are some alternatives to Tesseract OCR, TensorFlow?

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

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.

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

Related Comparisons

TensorFlow vs Tesseract OCR: What are the differences?

Programming Language: TensorFlow is written in Python, while Tesseract OCR is written in C++.
Functionality: TensorFlow is primarily used for deep learning and machine learning tasks, such as building and training neural networks, while Tesseract OCR is specifically designed for optical character recognition (OCR).
Supported Platforms: TensorFlow is a cross-platform library that can run on various operating systems like Windows, macOS, and Linux, while Tesseract OCR is also cross-platform but can run on fewer systems, including Windows and Linux.
Image Processing: TensorFlow provides a wide range of image processing capabilities, including image recognition, segmentation, and transformation, while Tesseract OCR is focused solely on text extraction from images and does not offer extensive image processing functionality.
Model Training: TensorFlow offers a comprehensive framework for training machine learning models, including the ability to define and optimize the model architecture, while Tesseract OCR is designed to work with pre-trained models and does not provide built-in support for training custom models.
Accuracy and Recognition: TensorFlow has a wider range of applications and can achieve higher accuracy in various tasks beyond OCR, while Tesseract OCR focuses specifically on text recognition and extraction and is optimized for OCR tasks.

TensorFlow vs Tesseract OCR

Overview