OCR-Agent: Agentic OCR with Capability and Memory Reflection

• URL: http://arxiv.org/abs/2602.21053v1
• Date: Tue, 24 Feb 2026 16:10:27 GMT
• Title: OCR-Agent: Agentic OCR with Capability and Memory Reflection
• Authors: Shimin Wen, Zeyu Zhang, Xingdou Bian, Hongjie Zhu, Lulu He, Layi Shama, Daji Ergu, Ying Cai,
• Abstract summary: Large Vision-Language Models (VLMs) have demonstrated significant potential on complex visual understanding tasks.<n>We propose a novel iterative self-correction framework that endows models with two key capabilities: Capability Reflection and Memory Reflection.<n> Experiments on the challenging OCRBench v2 benchmark show that OCR-Agent outperforms the current open-source SOTA model InternVL3-8B by +2.0 on English and +1.2 on Chinese subsets.
• Score: 5.8505408398110434
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Vision-Language Models (VLMs) have demonstrated significant potential on complex visual understanding tasks through iterative optimization methods.However, these models generally lack effective self-correction mechanisms, making it difficult for them to independently rectify cognitive biases. Consequently, during multi-turn revisions, they often fall into repetitive and ineffective attempts, failing to achieve stable improvements in answer quality.To address this issue, we propose a novel iterative self-correction framework that endows models with two key capabilities: Capability Reflection and Memory Reflection. This framework guides the model to first diagnose errors and generate a correction plan via Capability Reflection, then leverage Memory Reflection to review past attempts to avoid repetition and explore new solutions, and finally, optimize the answer through rigorous re-reasoning. Experiments on the challenging OCRBench v2 benchmark show that OCR-Agent outperforms the current open-source SOTA model InternVL3-8B by +2.0 on English and +1.2 on Chinese subsets, while achieving state-of-the-art results in Visual Understanding (79.9) and Reasoning (66.5) - surpassing even larger fine-tuned models. Our method demonstrates that structured, self-aware reflection can significantly enhance VLMs' reasoning robustness without additional training. Code: https://github.com/AIGeeksGroup/OCR-Agent.

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