Handwritten Text Recognition Techniques

Latest papers

• Color Independent Word Segmentation From Transcribed Bangla Passages

  Faias Satter, Noor Masrur, Sk. Md. Masudul Ahsan · 5 October 2026

  An optical character recognition(OCR) system can scan paper and extract text, making people's jobs easier. While numerous OCR systems are accessible in the software sector, finding a dependable equivalent solution for Bangla is tough. When it comes to handwritten texts, the case is even more rare. T…

• Open Vocabulary Word Recognition From Transcribed Bangla Texts

  Faias Satter, Sk. Md. Masudul Ahsan · 5 October 2026

  An optical character recognition (OCR) can scan a paper and extract text using technology, making people's jobs easier. While various OCR systems are available in the software industry, finding a reliable equivalent solution for Bangla takes much work. When it comes to handwritten texts, the situati…

• A Sobel-Gradient MLP Baseline for Handwritten Character Recognition

  Azam Nouri · 5 October 2026

  This study examines how much handwritten-character information is retained by a deliberately simple first-order edge representation. Instead of learning spatial filters, each input image is transformed by the fixed Sobel-Feldman operator into signed horizontal and vertical derivative maps, which are…

• Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents

  Ting Huang, Dongdong Wang, Mingqiu Liang, Siyang Lu · 2 October 2026

  Historical Manchu documents preserve invaluable linguistic and cultural heritage, yet their digitization is hindered by severe degradations and the scarcity of paired training data. Existing document restoration methods primarily optimize pixel-level reconstruction, which can produce visually plausi…

• STEPS: Scene Text Editing with Preserved Style Using Diffusion and Contrastive Style Encoding

  Nicolas Thiebaut, Nameer Hirschkind, Xiao Yu, Kyle Spence · 1 October 2026

  We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images. Scene Text Editing (STE), also known as Visual Text Editing, consists of changing the textual content in an image while conserving the original style, e.g. font,…

• Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts

  Anton Repushko, Elena Chepel · 1 October 2026

  Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would let scholars discover documents and literary works that have so far gone unread. Recognition systems for Ancient Greek papyri are in statu nascendi, …

• Developing an OCR model for Extracting Information from Invoices with Korean Language

  Xiem HoangVan, Phu TranQuang, Minh DinhBao, Tien VuHuu · 1 October 2026

  Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Making the extraction of important information crucial. The stored information serves different purposes. Korean language is the native language of about 80 million peo…

• PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligence

  GuangJian Team, Kaili Huang, Yongshuo Zhang, Bingtao Fu, Changjiang Jiang, Chenfan Qu, Chenfeng Zhang, Fangming Cui, Gaoyang Zhang, Jiangwei Xie, Jianshu Li, Jing Huang, Jingwen Bai, Mingqi Fang, Tao Fang, Weihong Zhang, Wenbo Du, Xiongfei Bai, Xuekang Zhu, Yinan Xia, Zhenming Wang, Jian Liu, Jingjing Liu, Xiang Qi, Weiqiang Wang · 30 September 2026

  Optical Character Recognition (OCR) is evolving from plain-text transcription toward general visual intelligence, requiring models to recognize, localize, and reason over textual information in complex visual environments. However, existing OCR systems often excel at only some tasks and struggle to …

• Age-Adaptive Handwriting Reconstruction from an IMU-Based Digital Pen through Shared Representations and Domain-Specific Heads

  Florent Imbert (LUT), Yann Soullard (IRISA, UR2, SHADOC), Eric Anquetil (INSA Rennes, IRISA, SHADOC), Hui Han (LUT) · 29 September 2026

  Digital pens are widely used to capture handwriting on digital devices, enabling precise trace recording and enhancing human-computer interaction. However, most are bundled with tablets and lack cross-brand compatibility. Recent digital pens equipped with kinematic sensors have emerged, designed for…

• PalmLeaf-VQA: A Multi-Script Visual Question Answering Benchmark for Historical Palm-Leaf Manuscript Understanding Across Diverse Regions

  Nimol Thuon, Jun Du, Panhapin Theang · 29 September 2026

  Historical manuscripts remain largely absent from modern vision-language benchmarks, leaving open how well multimodal large language models (MLLMs) handle culturally diverse, degraded, and non-Latin document images. We introduce \textbf{PalmLeaf-VQA}, a multi-script visual question answering benchma…

• Source-preserving alignment for robust evidence localization in scientific PDFS

  Zihao Liu, Wei Yang, Zixiao Dong, Chenshu Li, Longzhang Liu, Tao Tan, Hong Xie · 29 September 2026

  Scientific information-extraction systems often return a claim with an evidence string, which users must locate in the original PDF. This is challenging because the extracted evidence and PDF text layer are different representations: line wrapping, Unicode variants, superscripts, citation markers, a…

• Sorry Robot, Happy Human: Vision-Language Models Read Only One of Two Legible Typographic Layers

  Mert \.Incidelen, Yamen Kashkash, Asya Berker, Murat Aydo\u{g}an · 28 September 2026

  Vision-language models (VLMs), despite their success in optical character recognition (OCR) tasks, are vulnerable to typographic attacks and have a fragile structure for images with multiple text layers. In this study, the DecoyBench dataset was created using the Decoy Font method. The dataset consi…

• The Right Information Extraction Pipeline Depends on the Document: Accuracy-Energy Trade-offs for Small, Local Models

  Christoph Walser, Mauricio Fadel Argerich, Jonathan F\"urst · 28 September 2026

  Whether an information extraction pipeline should process page images or parsed text depends on the document, and the answer flips across the layout spectrum. We study this trade-off under a constraint that rules out (closed) cloud services: privacy-sensitive documents processed on-premise by small …

• MEVL-STP: Multi-Encoder and Vision Language Model for Arbitrarily Shaped Scene Text Spotting

  Aman Anand, Partha Pratim Roy, Shivakumara Palaiahnakote · 25 September 2026

  Scene text spotting remains challenging for arbitrarily shaped text instances such as curved signs and dense multi-oriented characters in natural images, where tightly coupled architectures propagate localization errors directly into recognition failures. We present a two-stage pipeline that combine…

• Diffusion Drafts, AR Verifies: Accelerating Document OCR with Self-Speculative Decoding

  Dohyun Kim, Sungjun Han, Hyungguk Kim, Yusik Kim, Jamin Shin, Paul Hongsuck Seo, Hongjoon Ahn · 23 September 2026

  Autoregressive OCR vision-language models accurately convert document images into text and structured markup, but require one sequential decoding step per output token, limiting inference speed. Unlike open-ended text generation, OCR outputs are strongly grounded in the input image, making diffusion…

• Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion

  Luca Foppiano, Sana Khamassi, Vipul Gupta · 23 September 2026

  Transforming scholarly PDFs into machine-readable fulltext remains a bottleneck for large-scale information systems. Recent vision-based parsers improve accuracy, but need GPUs and may introduce noise into the extracted text. GROBID, a modular font-stream parser running on CPU, is the de-facto stand…

• All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts

  Xingsong Ye, Yongkun Du, Jiaxin Zhang, Zhixian Li, Chong Sun, Chen Li, Jing Lyu, Lianwen Jin, Zhineng Chen · 22 September 2026

  Multilingual scene text recognition (STR) remains challenging due to the scarcity of training data for most languages and the difficulty of serving diverse scripts within a single model. Existing solutions either deploy one recognizer per language, inflating cost and introducing error accumulation, …

• Mind the Gaps: A Curated Benchmark for Form Field Detection

  Iheb Brini, Omar Moured, Hamza Gbada, Elisa Barney · 22 September 2026

  Form Field Detection (FFD) is a fundamental component of document understanding systems, enabling applications ranging from large-scale industrial digitization to accessible form interaction for automated analysis. Unlike conventional object detection tasks, FFD is inherently challenging because fie…

• From Retrieval to Recognition:How Vision--Language Models Become OCR Specialists

  Yuanxiang Huangfu, Hanmeng Zhong, Linqing Chen, Jeffrey Tiong Jee Hui · 21 September 2026

  Does a general vision--language model acquire specialized OCR ability by developing a new reading circuit or by reusing an existing mechanism? We address this question in the setting of full-sequence OCR, rather than local-answer retrieval. Using an evidence-grounded protocol with held-out causal in…

• Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction

  Abhishek Bhandari, Gaurav Harit · 21 September 2026

  In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanagari script remains entirely unexplored. We present the first systematic evaluation of LLMs (3B-32B) for post-OCR correction in Hindi and Marathi, com…

• WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing

  Hao Yu, Kang Liu, Linnan Zhao, Jiabo Zhan, Chong Sun, Chen Li, Jing Lyu · 18 September 2026

  Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are biased toward common document types and clean digital pages, while expanding coverage alone does not specify how to address …

• A Free Lunch? Adapting PP-OCRv6 for Historical Text Recognition

  Benjamin Kiessling (ALMAnaCH) · 18 September 2026

  Despite impressive reported scores, large vision-language models have seen limited practical uptake in historical automatic text recognition because of their computational cost, dependence on large-scale pretraining, and hallucination. Historical ATR therefore continues to rely largely on compact CR…

• LinePilot Digitizer: Line-Plot Recovery with Manual and Automatic Calibration

  Fengbo Ma, Rayan Akhtar, Aakash H. Joshi, Xiaoting Li, Haijian Sun, Zhen Xiang, Xianyan Chen, Yiping Zhao · 18 September 2026

  Recovering numerical series from line plots requires accurate axis calibration and reliable curve extraction. We present LinePilot Digitizer (LinePilot), which combines continuous color-based curve recovery with three calibration modes: LinePilot (standard), LinePilot (enhanced), and LinePilot (OCR)…

• Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents

  Yichao Jin, Yushuo Wang, Yuxuan Han, Kwan Ching Yee Sonia, Weiyang Song, Chiu Jin-Chun Kent, Wong Chong Hwee, Wong Tiong Kiat, Kenneth Zhu Ke, Jingyuan Zhao · 18 September 2026

  Straight-through processing (STP) on extracted key-value fields from financial documents without human review requires a calibrated probability together with a bounded guarantee on the residual error of the auto-approved tier. The emergence of modern Vision Language Models (VLMs) provides an out-of-…

• FROD: Feature Matching Residual Denoising Oracle Bone Decipher

  Yanbin Hou, Biao Xiong, Guojun Xu, Jianwen Xiang, Cheng Tan, Yanchao Yang, Junwei Zhou · 16 September 2026

  Oracle bone script (OBS), one of the earliest Chinese writing systems, plays an important role in the study of Chinese etymology. Traditional decipherment relies heavily on domain experts who analyze characters through semantic context and structural evolution. To assist this labor-intensive process…