TOP PYTHON LIBRARIES FOR OCR (OPTICAL CHARACTER RECOGNISATION)

Optical character recognition (OCR) is a field of computer science and artificial intelligence that focuses on the automated recognition of text in images and documents. Python is a popular programming language for OCR, and there are a wide variety of libraries available for working with text recognition. Here are the top 10 Python libraries for OCR:

1. Tesseract
2. OCRopus
3. GOCR
4. CuneiForm
5. Ocrad
6. Ocrmypdf
7. Ocrfeeder
8. Ocr4all

1. TESSERACT

This is an open-source OCR engine developed by Google. It is highly accurate and supports a wide range of languages. There are a number of Python wrappers available for Tesseract, including pytesseract and tesserocr.

2. OCROPUS

This is an open-source OCR system developed by Google. It includes a range of tools and algorithms for working with text recognition, including support for layout analysis and document preparation.

3. GOCR

This is an open-source OCR engine developed by the German Research Center for Artificial Intelligence. It is highly accurate and supports a wide range of languages. There is a Python wrapper available for GOCR, called pygocr.

4. CUNEIFORM

This is an open-source OCR engine developed by Cognitive Technologies. It is highly accurate and supports a wide range of languages. There is a Python wrapper available for CuneiForm, called pycuneiform.

5. OCRAD

This is an open-source OCR engine developed by the Free Software Foundation. It is highly accurate and supports a wide range of languages. There is a Python wrapper available for Ocrad, called pyocrad.

6. OCRMYPDF

This is an open-source tool for adding OCR text to PDF files. It uses Tesseract as the OCR engine and includes a range of tools for working with text recognition.

7. OCRFEEDER

This is an open-source graphical OCR application. It includes a range of tools and algorithms for working with text recognition, including support for layout analysis and document preparation.

8. OCR4ALL

This is an open-source OCR system developed by the University of Würzburg. It includes a range of tools and algorithms for working with text recognition, including support for layout analysis and document preparation.

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