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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Pure-Rust, CPU-only OCR engine for Baidu Unlimited-OCR (a DeepSeek-OCR-derived 3B MoE VLM). Five-model zoo, custom int8 kernels, no ML framework, no Python, no GPU.
| Date | Stars |
|---|---|
| 2026-07-24 | 269 |
| 2026-07-25 | 271 |
| 2026-07-28 | 274 |
| 2026-07-30 | 274 |
| 2026-08-06 | 274 |
Today
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35.0
growth rate 0.00%/day
# franken_ocr <div align="center"> <img src="franken_ocr_illustration.webp" alt="franken_ocr - Pure-Rust CPU-only OCR for Baidu Unlimited-OCR"> </div> <div align="center"> [](./LICENSE) [](https://github.com/Dicklesworthstone/franken_ocr/tree/v0.7.2) [](https://github.com/Dicklesworthstone/franken_ocr/releases/tag/v0.7.2) [](#quick-example) [](https://doc.rust-lang.org/edition-guide/rust-2024/) [](./rust-toolchain.toml) [](https://github.com/rust-secure-code/safety-dance/) [](https://huggingface.co/baidu/Unlimited-OCR) [](#conformance-and-release-evidence) </div> **A pure-Rust, memory-safe, CPU-only OCR engine for a small family of hand-ported vision-language models.** Baidu Unlimited-OCR is the fast default for document OCR, GOT-OCR2 handles specialized structured formats, SmolVLM2 handles image description and VQA, OneChart extracts chart data, and Polyphonic-TrOMR turns full scanned sheet-music pages or staff crops into MusicXML through `--task music`. All five runtime models are available through `focr pull`; TrOMR publishes both the 61 MB int8 default artifact and an 86 MB f32 reference artifact. The v0.7.0 Unlimited-OCR artifact uses the conservative exact recipe and passes the hard-page termination and complete 20-page corpus budget. The models run through model-specific Rust kernels and need no general ML framework, Python, CUDA, FFI at inference, or GPU. <div align="center"> <h3>Quick Install</h3> ```bash curl -fsSL https://raw.githubusercontent.com/Dicklesworthstone/franken_ocr/main/install.sh | bash ``` </div> The installer detects your platform, resolves the latest published GitHub binary release (currently `v0.7.2`), verifies the downloaded asset by SHA256, and puts `focr` on your PATH. Install compatible model weights separately; after they are present, inference is offline. ### Current Release `v0.7.2` ships raw executables for macOS Apple Silicon, macOS Intel, Linux x86-64, Linux ARM64, Windows x86-64, and Windows ARM64, each with a SHA256 sidecar. Its embedded schema-v2 manifest continues to pin the `v0.7.0` 4,157,448,783-byte Unlimited-OCR artifact using recipe `unlimited-ocr-ffn-int8-attn-bf16-lmhead-bf16-v1`; `focr pull` verifies all three part hashes and the reassembled file before installation. The exact-recipe artifact emits EOS on `page0590` and passes the complete 20-page corpus budget at aggregate normalized CER 0.19307925. The release does not claim the strict three-party OpenPGP certificate: the committed fail-closed finalizer requires three independently controlled registry-pinned signers and production audit receipts, which are not available in this release process. The local corpus, model-census, installer, and binary checks are evidence, not a substitute for that governance claim. --- ## TL;DR **The problem.** Baidu Unlimited-OCR is a strong document-parsing model: Markdown, tables, LaTeX, reading order, many pages in one pass. The official stack is Python plus CUDA. Most machines that need OCR (laptops, CI runners, agent hosts, edge boxes) have no usable GPU, and a Python plus CUDA dependency is heavy to ship and awkward to embed. **The solution.** `franken_ocr` is a library plus a single-binary CLI (`focr`) that runs the ready mod
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:66795ca99dad9eba, topic:ocr, readme:document parsing, name:ocr
matched fp:66795ca99dad9eba, topic:quantization, desc:int8, readme:int8
matched fp:66795ca99dad9eba, topic:moe, readme:model weights
matched fp:66795ca99dad9eba, topic:vision-language-model, readme:vision-language