ABot-OCR Technical Report

• URL: http://arxiv.org/abs/2605.27978v1
• Date: Wed, 27 May 2026 05:16:21 GMT
• Title: ABot-OCR Technical Report
• Abstract summary: ABot-OCR is an end-to-end vision-language model that transcribes a page image directly into clean markdown in a single forward pass.<n>We develop a dedicated data engine to provide large-scale, structurally consistent supervision.<n>ABot-OCR achieves state-of-the-art scores of 92.81 and 93.30 among all end-to-end systems.
• Score: 1.2258341760650364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce ABot-OCR, an end-to-end vision-language model that transcribes a page image directly into clean Markdown in a single forward pass. By doing so, our approach completely eliminates the need for brittle modular orchestration. To maximize parsing fidelity, we develop a dedicated data engine to provide large-scale, structurally consistent supervision. Furthermore, we propose Decoupled Heterogeneous Document Optimization, a structure-constrained reinforcement learning method that sharpens textual accuracy and strictly enforces markup well-formedness beyond supervised fine-tuning alone. Extensive evaluations demonstrate the superior performance of our framework. On the OmniDocBench v1.5 and v1.6 benchmarks, ABot-OCR achieves state-of-the-art scores of 92.81 and 93.30 among all end-to-end systems, substantially narrowing the performance gap relative to strong pipeline baselines. Finally, comprehensive multilingual text recognition across ten diverse languages further confirms the robust generalizability of ABot-OCR.

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