Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards

• URL: http://arxiv.org/abs/2609.03181v1
• Date: Wed, 02 Sep 2026 21:49:21 GMT
• Title: Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards
• Abstract summary: Jina-OCR-v1 is an end-to-end document parsing model built to serve on low-budget GPUs.<n>It combines the compressed-vision encoder and the 3B mixture-of-experts decoder of DeepSeek-OCR.<n>It scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, and reaches the highest page throughput in our comparison at 2.57 pages per second.
• Score: 44.67582833168009
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Jina-OCR-v1, an end-to-end document parsing model built to serve on low-budget GPUs. It combines the compressed-vision encoder and the 3B mixture-of-experts decoder of DeepSeek-OCR, which activates about 570M parameters per token, with a FastMTP speculative decoding head that shares a single draft block recursively across K=3 prediction steps. Greedy verification makes decoding lossless. Post-training combines instruction alignment, robustness fine-tuning on difficult documents, and GRPO under dense verifiable rewards: deterministic formula, table, and structural checks that award partial credit. The training data mixes cleaned public corpora with targeted synthetic pages. At the default dynamic-resolution setting, Jina-OCR-v1 scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, and reaches the highest page throughput in our comparison at 2.57 pages per second. On a low-budget GPU such as the NVIDIA L4, FastMTP doubles decoding speed over greedy autoregressive decoding. The model is publicly available at https://huggingface.co/jinaai/jina-ocr-v1.

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