Best OCR Software for GitHub

Find and compare the best OCR software for GitHub in 2025

Use the comparison tool below to compare the top OCR software for GitHub on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    iText Reviews
    Previously known as iText, we are now a part of Apryse. With optimized technology and a comprehensive suite of tools, Apryse simplifies even the most complex projects, taking you further, faster. Committed to feature-rich products that are made better, Apryse offers superior document solutions across all applications and enterprise workflows. With iText by Apryse, our diverse customer base includes more than half of the Fortune 500 companies, as well as many government agencies and small companies alike. Our software has grown out of the open source space, and we still believe in the value of open source software. Our core library iText 7 Community and earlier versions iText 5, and iText 2 are all available under the AGPL license. We do offer commercial licensing for customers that do not wish to comply with AGPL and would like to keep their source code private. You may have used iText when you: - received a boarding pass from an airline, - received a PDF invoice or receipt, - received a PDF document after filling in a form, - and many more. For more information, visit the Apryse website.
  • 2
    HunyuanOCR Reviews
    Tencent Hunyuan represents a comprehensive family of multimodal AI models crafted by Tencent, encompassing a range of modalities including text, images, video, and 3D data, all aimed at facilitating general-purpose AI applications such as content creation, visual reasoning, and automating business processes. This model family features various iterations tailored for tasks like natural language interpretation, multimodal comprehension that combines vision and language (such as understanding images and videos), generating images from text, creating videos, and producing 3D content. The Hunyuan models utilize a mixture-of-experts framework alongside innovative strategies, including hybrid "mamba-transformer" architectures, to excel in tasks requiring reasoning, long-context comprehension, cross-modal interactions, and efficient inference capabilities. A notable example is the Hunyuan-Vision-1.5 vision-language model, which facilitates "thinking-on-image," allowing for intricate multimodal understanding and reasoning across images, video segments, diagrams, or spatial information. This robust architecture positions Hunyuan as a versatile tool in the rapidly evolving field of AI, capable of addressing a diverse array of challenges.
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