OpenBMB releases MiniCPM-OCR 1.0 and Jev quality judge
OpenBMB released two open-source models for document OCR: the 1.3B-parameter MiniCPM-OCR 1.0, which parses Chinese and English documents, and the 2B-parameter MiniCPM-OCR Jev, which scores OCR results for text, formulas, and tables. OpenBMB reported a 93.68 score for MiniCPM-OCR 1.0 on Real5-OmniDocBench and a 55% processing-efficiency gain from DSpark decoding; it also reported a 0.936 AUROC for Jev. The code and model weights are released under Apache 2.0, and the reports list support for vLLM, SGLang, llama.cpp, and Ollama.
Jev can score outputs from other OCR parsers, allowing existing document-processing pipelines to add a separate quality-checking step without changing their parser. The two models' relatively small parameter counts and local deployment options may make this combined workflow easier to adopt.
| Most OCR models focus on reading documents. MiniCPM-OCR adds a second piece: a model that checks whether the OCR result is actually trustworthy. OpenBMB just released two open-source models: The interesting part is Jev can also evaluate outputs from other OCR systems, so you don't necessarily have to replace your existing pipeline. Both models are open source, with support for vLLM, SGLang, llama.cpp, and Ollama. Hugging Face Links: https://huggingface.co/openbmb/MiniCPM-OCR-Jev https://huggingface.co/openbmb/MiniCPM-OCR-1.0 submitted by /u/Equivalent-Grass-527 [link] [comments] |