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Tesseract is an optical character recognition engine for various operating systems. [5] It is free software, released under the Apache License. [1] [6] [7] Originally developed by Hewlett-Packard as proprietary software in the 1980s, it was released as open source in 2005 and development was sponsored by Google in 2006.
Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner. Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and ...
scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language. [2] It includes algorithms for segmentation , geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection , and more. [ 3 ]
Python Features a full user interface and has a command-line tool for automatic operations. Has its own segmentation algorithm but uses system-wide OCR engines like Tesseract or Ocrad
Asprise OCR is a commercial optical character recognition and barcode recognition SDK library that provides an API to recognize text as well as barcodes from images (in formats like JPEG, PNG, TIFF, PDF, etc.) and output in formats like plain text, XML and searchable PDF. Asprise OCR has been in active development since 1997.
Originally, the software was developed in C++, Python and Lua with Jam as a build system. A complete refactoring of the source code in Python modules was done and released in version 0.5 (June 2012). [11] Initially, Tesseract was used as the only text recognition module. Since 2009 (version 0.4) Tesseract was only supported as a plugin.
The text areas with text lines in the images are first recognized manually or automatically (segmentation). The text lines are then transcribed manually or automatically. [4] Both automatic segmentation and text recognition can be trained using manually created or corrected examples (ground truth). The new models created in this way can be ...
Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be ...