DianJin-OCR-R1: Enhancing OCR Capabilities via a Reasoning-and-Tool Interleaved Vision-Language Model

• URL: http://arxiv.org/abs/2508.13238v2
• Date: Thu, 04 Sep 2025 08:05:29 GMT
• Title: DianJin-OCR-R1: Enhancing OCR Capabilities via a Reasoning-and-Tool Interleaved Vision-Language Model
• Authors: Qian Chen, Xianyin Zhang, Lifan Guo, Feng Chen, Chi Zhang,
• Abstract summary: Large vision-language models (LVLMs) are prone to hallucinations--generating words that do not exist in input images.<n>We propose DianJin-OCR-R1, a reasoning-and-tool interleaved VLMs trained on domain-specific datasets.
• Score: 9.557159109747372
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in large vision-language models (LVLMs) have enabled a new paradigm of end-to-end document image parsing, excelling in Optical Character Recognition (OCR) tasks such as text, table, and formula recognition. However, generative LVLMs, similarly to large language models (LLMs), are prone to hallucinations--generating words that do not exist in input images. Furthermore, LVLMs are designed for general purposes and tend to be less effective on OCR tasks compared to expert models that are trained on domain-specific datasets. In this paper, we propose DianJin-OCR-R1, a reasoning-enhanced framework designed to address these limitations through training reasoning-and-tool interleaved VLMs. Given a recognition instruction, our DianJin-OCR-R1 model first recognizes the content in the input image by its own OCR capabilities, and then calls other tools (i.e., other expert models) to obtain their results as references, finally "looks again" the image and rethinks about the reasoning process to provide the final recognized content. Since architectures of expert models are tailored for specific OCR tasks, which makes them less prone to hallucinations, their results can help VLMs mitigate hallucinations. We evaluate our model on ReST and OmniDocBench, and experimental results show that our DianJin-OCR-R1 models consistently outperform their non-reasoning counterparts and expert OCR models, which proves the effectiveness of our method. Additionally, the results indicate that enhancing expert models, which are typically small and easy to iterate, enable performance improvements for VLMs.

Related papers

• SimpleOCR: Rendering Visualized Questions to Teach MLLMs to Read [43.28273039987167]
  We introduce the Visualized-Question (VQ) setting, where text queries are rendered directly onto images.<n>Despite possessing strong OCR capabilities, models suffer a performance degradation of up to 12.7% in the VQ setting.<n>We propose SimpleOCR, a plug-and-play training strategy that imposes a structural constraint on the learning process.
  arXiv  Detail & Related papers  (2026-02-25T21:36:30Z)
• OCR-Agent: Agentic OCR with Capability and Memory Reflection [5.8505408398110434]
  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.
  arXiv  Detail & Related papers  (2026-02-24T16:10:27Z)
• ViSS-R1: Self-Supervised Reinforcement Video Reasoning [84.1180294023835]
  We introduce a novel self-supervised reinforcement learning GRPO algorithm (Pretext-GRPO) within the standard R1 pipeline.<n>We also propose the ViSS-R1 framework, which streamlines and integrates pretext-task-based self-supervised learning directly into the MLLM's R1 post-training paradigm.
  arXiv  Detail & Related papers  (2025-11-17T07:00:42Z)
• CIR-CoT: Towards Interpretable Composed Image Retrieval via End-to-End Chain-of-Thought Reasoning [93.05917922306196]
  Composed Image Retrieval (CIR) aims to find a target image from a reference image and a modification text.<n>CIR-CoT is the first end-to-end retrieval-oriented MLLM designed to integrate explicit Chain-of-Thought (CoT) reasoning.
  arXiv  Detail & Related papers  (2025-10-09T09:41:45Z)
• Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency [31.095908827004695]
  Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks.<n>They struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges.<n>We introduce a novel fine-tuning paradigm, named Synchronously Self-Reviewing (SSR) its OCR proficiency, inspired by the concept "Bilingual Cognitive Advantage"
  arXiv  Detail & Related papers  (2025-07-11T05:02:06Z)
• VISTA-OCR: Towards generative and interactive end to end OCR models [3.7548609506798494]
  VISTA-OCR is a lightweight architecture that unifies text detection and recognition within a single generative model.<n>Built on an encoder-decoder architecture, VISTA-OCR is progressively trained, starting with the visual feature extraction phase.<n>To enhance the model's capabilities, we built a new dataset composed of real-world examples enriched with bounding box annotations and synthetic samples.
  arXiv  Detail & Related papers  (2025-04-04T17:39:53Z)
• CoLLM: A Large Language Model for Composed Image Retrieval [76.29725148964368]
  Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query.<n>We present CoLLM, a one-stop framework that generates triplets on-the-fly from image-caption pairs.<n>We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts.
  arXiv  Detail & Related papers  (2025-03-25T17:59:50Z)
• OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles [91.88062410741833]
  We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning.<n>We show that OpenVLThinker-7B consistently advances performance across six benchmarks demanding mathematical and general reasoning.
  arXiv  Detail & Related papers  (2025-03-21T17:52:43Z)
• Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval [50.72924579220149]
  Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the reference image, text, target image. We propose a new semi-supervised CIR approach where we search for a reference and its related target images in auxiliary data.
  arXiv  Detail & Related papers  (2024-04-23T21:00:22Z)
• Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer [12.966765239586994]
  This paper proposes DLoRA-TrOCR, a parameter-efficient hybrid text spotting method based on a pre-trained OCR Transformer.<n>By embedding a weight-decomposed DoRA module in the image encoder and a LoRA module in the text decoder, this method can be efficiently fine-tuned on various downstream tasks.<n> Experiments show that our proposed DLoRA-TrOCR outperforms other parameter-efficient fine-tuning methods in recognizing complex scenes with mixed handwritten, printed, and street text.
  arXiv  Detail & Related papers  (2024-04-19T09:28:16Z)
• Vision-by-Language for Training-Free Compositional Image Retrieval [78.60509831598745]
  Compositional Image Retrieval (CIR) aims to retrieve the relevant target image in a database. Recent research sidesteps this need by using large-scale vision-language models (VLMs) We propose to tackle CIR in a training-free manner via Vision-by-Language (CIReVL)
  arXiv  Detail & Related papers  (2023-10-13T17:59:38Z)
• Retrieval-Enhanced Contrastive Vision-Text Models [61.783728119255365]
  We propose to equip vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks.
  arXiv  Detail & Related papers  (2023-06-12T15:52:02Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)
• Donut: Document Understanding Transformer without OCR [17.397447819420695]
  We propose a novel VDU model that is end-to-end trainable without underpinning OCR framework. Our approach achieves state-of-the-art performance on various document understanding tasks in public benchmark datasets and private industrial service datasets.
  arXiv  Detail & Related papers  (2021-11-30T18:55:19Z)
• Characteristic Regularisation for Super-Resolving Face Images [81.84939112201377]
  Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data. This renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. We formulate a method that joins the advantages of conventional SR and UDA models.
  arXiv  Detail & Related papers  (2019-12-30T16:27:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.