The paper presents a local traditional OCR pipeline that can be iteratively fine‑tuned on both layout and appearance levels of complex historical Sanskrit manuscripts. By adapting to the specific manuscript distribution, the pipeline improves transcription accuracy across subsequent pages, reducing the need for costly human annotation. The authors apply the method to three manuscripts, release a dataset with detailed layout and Unicode annotations in PAGE‑XML format, and benchmark the results against leading multimodal large language models.
The paper presents a local traditional OCR pipeline that can be iteratively fine‑tuned on both layout and appearance levels of complex historical Sanskrit manuscripts. By adapting to the specific manuscript distribution, the pipeline reduces human annotation effort and improves transcription accuracy across subsequent pages. The authors apply this method to three manuscripts, release a dataset with detailed layout and Unicode annotations in PAGE‑XML format, and benchmark the pipeline against leading multimodal large language models.
By Kartik Chincholikar, Kaushik Gopalan, Mihir Hasabnis
arXiv:2610.18238v1 Announce Type: cross
Abstract: Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract p...
By Qian Chen, Xianyin Zhang, Lifan Guo, Chi Zhang
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.
The paper investigates the gap between retrieval and reading in document vision‑language models, showing that even when the correct page is retrieved, the model often fails to use the text on that page. By comparing answers derived from page images alone versus images plus extracted OCR text, the authors demonstrate that adding OCR text can improve strict accuracy by 13 to 16 points on their FoveDoc‑Bench benchmark. The study also reveals that OCR benefits textual evidence but not charts or figures, and that its advantage diminishes as retrieval quality worsens.
By Qingtao Xia, Siyao Cheng, Jiahua Bao, Jiaxing Du, Jie Liu
HunyuanOCR-1.5 is a lightweight, end‑to‑end OCR‑specialized vision‑language model that unifies document parsing, text spotting, information extraction, text‑image translation, and multi‑image document understanding. It builds on the HunyuanOCR‑1.0 architecture, improving efficiency with DFlash‑based OCR decoding for faster inference (6.37× Transformer speedup, 2.14× under vLLM) and enhancing capability through an Agentic Data Flow system that autonomously constructs high‑quality training data for long‑tail OCR tasks. The model achieves top‑tier performance on OmniDocBench v1.6 and sets new milestones in ancient‑script OCR, chart/table parsing, multilingual parsing, and hallucination evaluation, while remaining lightweight for deployment.
By Gengluo Li, Xingyu Wan, Shangpin Peng, Weinong Wang, Hao Feng, Yongkun Du, Binghong Wu, Zheng Ruan, Zhiqiong Lu, Liang Wu, Pengyuan Lyu, Huawen Shen, Zibin Lin, Shijing Hu, Jieneng Yang, Hongbing Wen, Guanghua Yu, Hong Liu, Bochao Wang, Can Ma, Han Hu, Chengquan Zhang, Yu Zhou
arXiv:2606. 06242v1 Announce Type: cross Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables.
By AJ Carl P. Dy, Aivin V. Solatorio
arXiv:2609.08330v1 Announce Type: new
Abstract: Table detection is a core task in document analysis, supporting downstream applications such as information retrieval, document reconstruction, and vis...
By Dhruv Kudale, Udhay Brahmi, Ganesh Ramakrishnan
Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for extracting visual content from documents are largely built around generic document layout analysis, where figures and tables are treated as uniformly relevant document objects rather than semantically meaningful analytical artifacts.
arXiv:2607. 16203v1 Announce Type: cross Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information.
By Zihan Xu, Puzhen Wu, Lawrence Chun Man Lau, Wei Liu, Sirui Li, Yifan Peng, Yihao Ding
arXiv:2606. 28796v1 Announce Type: cross Abstract: Government documents in India are predominantly issued in regional languages such as Marathi, creating substantial accessibility barriers for non-native readers, interstate administrative bodies, and policy analysts.
By Manasi Waghe, Danish Chandargi, Mohammad Aamir Rayyan, Raviraj Joshi, A. R. Deshpande
arXiv:2607. 08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents.
By Maud Ehrmann, Emanuela Boros, Juri Opitz, Andrianos Michail, Florian Wagner, Simon Clematide