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doctr
docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
Tesseract is widely known to be "meh" at this point.
If you look at RAG frameworks as one example they'll typically use/support a variety of implementations. Tesseract is almost always supported but it's rarely ideal with projects like Unstructured[0] and DocTR[1] being preferred. By leveraging more-or-less SOTA vision models[2][3] they embarrass Tesseract.
I haven't compared them to the Apple Vision framework but they're absolutely better than Tesseract and potentially even Apple Vision.
[0] - https://github.com/Unstructured-IO/unstructured-inference
[1] - https://github.com/mindee/doctr
[2] - https://github.com/mindee/doctr#models-architectures
[3] - https://github.com/Unstructured-IO/unstructured-inference#mo...
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Judoscale
Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
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ocrmac
A python wrapper to extract text from images on a mac system. Uses the vision framework from Apple.
Nice post, OP! I was super impressed with the Apple's vision framework. I used it on a personal project involving the OCRing of tens of thousands of spreadsheet screenshots and ingesting them into a postgres database.
I used a combination of RHetTbull's vision.py (for the actual implementation) [1] + ocrmac (for experimentation) [2] and was pleasantly surprised by the performance on my i7 6700k hackintosh.
I wouldn't call myself a programmer but I can generally troubleshoot anything if given enough time, but it did cost time.
[1]: https://gist.github.com/RhetTbull/1c34fc07c95733642cffcd1ac5...
[2]: https://github.com/straussmaximilian/ocrmac
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Tesseract is widely known to be "meh" at this point.
If you look at RAG frameworks as one example they'll typically use/support a variety of implementations. Tesseract is almost always supported but it's rarely ideal with projects like Unstructured[0] and DocTR[1] being preferred. By leveraging more-or-less SOTA vision models[2][3] they embarrass Tesseract.
I haven't compared them to the Apple Vision framework but they're absolutely better than Tesseract and potentially even Apple Vision.
[0] - https://github.com/Unstructured-IO/unstructured-inference
[1] - https://github.com/mindee/doctr
[2] - https://github.com/mindee/doctr#models-architectures
[3] - https://github.com/Unstructured-IO/unstructured-inference#mo...
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aichat
All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
use LLMs (gpt-4-vision or LLaVA) with aichat
`aichat -f tmp/test.png -- output only text in the image`
https://github.com/sigoden/aichat
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I had good repeated success extracting tables from PDFs using Camelot (Python, https://github.com/camelot-dev/camelot)
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
