OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

• URL: http://arxiv.org/abs/2601.21639v2
• Date: Wed, 04 Feb 2026 12:53:33 GMT
• Title: OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models
• Authors: Yufeng Zhong, Lei Chen, Xuanle Zhao, Wenkang Han, Liming Zheng, Jing Huang, Deyang Jiang, Yilin Cao, Lin Ma, Zhixiong Zeng,
• Abstract summary: OCRVerse is the first holistic OCR method in end-to-end manner that enables unified text-centric OCR and vision-centric OCR.<n>We constructe comprehensive data engineering to cover a wide range of text-centric documents, as well as vision-centric rendered composites, including charts, web pages and scientific plots.
• Score: 13.375954596561469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of large vision language models drives the demand for managing, and applying massive amounts of multimodal data, making OCR technology, which extracts information from visual images, increasingly popular. However, existing OCR methods primarily focus on recognizing text elements from images or scanned documents (Text-centric OCR), neglecting the identification of visual elements from visually information-dense image sources (Vision-centric OCR), such as charts, web pages and science plots. In reality, these visually information-dense images are widespread on the internet and have significant real-world application value, such as data visualization and web page analysis. In this technical report, we propose OCRVerse, the first holistic OCR method in end-to-end manner that enables unified text-centric OCR and vision-centric OCR. To this end, we constructe comprehensive data engineering to cover a wide range of text-centric documents, such as newspapers, magazines and books, as well as vision-centric rendered composites, including charts, web pages and scientific plots. Moreover, we propose a two-stage SFT-RL multi-domain training method for OCRVerse. SFT directly mixes cross-domain data to train and establish initial domain knowledge, while RL focuses on designing personalized reward strategies for the characteristics of each domain. Specifically, since different domains require various output formats and expected outputs, we provide sufficient flexibility in the RL stage to customize flexible reward signals for each domain, thereby improving cross-domain fusion and avoiding data conflicts. Experimental results demonstrate the effectiveness of OCRVerse, achieving competitive results across text-centric and vision-centric data types, even comparable to large-scale open-source and closed-source models.

Related papers

• AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation [35.07704681580893]
  We introduce AgenticOCR, a dynamic parsing paradigm that transforms optical character recognition (OCR) into a query-driven, on-demand extraction system.<n>By autonomously analyzing document layout in a "thinking with images" manner, AgenticOCR identifies and selectively recognizes regions of interest.<n>AgenticOCR has the potential to serve as the "third building block" of the visual document RAG stack.
  arXiv  Detail & Related papers  (2026-02-27T16:09:38Z)
• When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study [48.67151986743594]
  We revisit representation design for Semantic ID learning by treating text as a visual signal.<n>We conduct a systematic empirical study of OCR-based text representations, obtained by rendering item descriptions into images.<n>We find that OCR-text consistently matches or surpasses standard text embeddings for Semantic ID learning in both unimodal and multimodal settings.
  arXiv  Detail & Related papers  (2026-01-21T06:18:57Z)
• Exploring a Unified Vision-Centric Contrastive Alternatives on Multi-Modal Web Documents [99.62178668680578]
  We propose Vision-Centric Contrastive Learning (VC2L), a unified framework that models text, images, and their combinations using a single vision transformer.<n> VC2L operates entirely in pixel space by rendering all inputs, whether textual, visual, or combined, as images.<n>To capture complex cross-modal relationships in web documents, VC2L employs a snippet-level contrastive learning objective that aligns consecutive multimodal segments.
  arXiv  Detail & Related papers  (2025-10-21T14:59:29Z)
• 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)
• See then Tell: Enhancing Key Information Extraction with Vision Grounding [32.445618057103324]
  We introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding.<n>To enhance the model's seeing capabilities, we collect extensive structured table recognition datasets.<n>Our approach demonstrates substantial advancements in KIE performance, achieving state-of-the-art results on publicly available datasets.
  arXiv  Detail & Related papers  (2024-09-29T06:21:05Z)
• VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding [18.609441902943445]
  VisFocus is an OCR-free method designed to better exploit the vision encoder's capacity by coupling it directly with the language prompt.<n>We pair the architecture enhancements with a novel pre-training task, using language masking on a snippet of the document text fed to the visual encoder.<n>Our experiments demonstrate that this prompt-guided visual encoding approach significantly improves performance.
  arXiv  Detail & Related papers  (2024-07-17T14:16:46Z)
• EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge [1.8434042562191815]
  EffOCR is a novel open-source optical character recognition (OCR) package. It meets both the computational and sample efficiency requirements for liberating texts at scale. EffOCR is cheap and sample efficient to train, as the model only needs to learn characters' visual appearance and not how they are used in sequence to form language.
  arXiv  Detail & Related papers  (2023-10-16T04:20:16Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)
• Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution [48.693941280097974]
  We propose a large-scale dataset consisting of camera images for visual information extraction (VIE) We propose a novel framework for end-to-end VIE that combines the stages of OCR and information extraction in an end-to-end learning fashion. We evaluate the existing end-to-end methods for VIE on the proposed dataset and observe that the performance of these methods has a distinguishable drop from SROIE to our proposed dataset due to the larger variance of layout and entities.
  arXiv  Detail & Related papers  (2023-05-12T14:11:47Z)
• One-shot Key Information Extraction from Document with Deep Partial Graph Matching [60.48651298832829]
  Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios. Existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for different types of documents. We propose a deep end-to-end trainable network for one-shot KIE using partial graph matching.
  arXiv  Detail & Related papers  (2021-09-26T07:45:53Z)
• Rethinking Text Line Recognition Models [57.47147190119394]
  We consider two decoder families (Connectionist Temporal Classification and Transformer) and three encoder modules (Bidirectional LSTMs, Self-Attention, and GRCLs) We compare their accuracy and performance on widely used public datasets of scene and handwritten text. Unlike the more common Transformer-based models, this architecture can handle inputs of arbitrary length.
  arXiv  Detail & Related papers  (2021-04-15T21:43:13Z)

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.