https://ym-xu.github.io/living-survey/fields/document-understanding/#tag=historicalHandwritten text, manuscripts, archives and historical newspapers.Fri, 13 Nov 2026 14:11:24 +0000https://arxiv.org/abs/2611.13752https://arxiv.org/abs/2611.13752Tue, 10 Nov 2026 00:00:00 +0000Fonseca Cacho, Jorge Ramón OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such p…https://arxiv.org/abs/2611.08091https://arxiv.org/abs/2611.08091Thu, 05 Nov 2026 00:00:00 +0000Ismail El Jamiy, Khadija Msalek, Guillaume Loizelet, Farah Benamara, et al. Despite recent progress in handwritten text recognition, medieval Arabic scientific manuscripts remain an open challenge for current vision-language models (VLMs) due to cursive ligatures, variable diacritization, and more importantly the lack of training data. This paper aims to address this gap, by proposing the firs…https://arxiv.org/abs/2610.40233https://arxiv.org/abs/2610.40233Thu, 29 Oct 2026 00:00:00 +0000Chutong Meng, Alec J. Bateman, Antonios Anastasopoulos While mining parallel sentences from web pages has proven effective for low-resource machine translation, the majority of the world's languages have little to no web presence. What they do have, though, are resources that are the result of centuries of efforts by linguists, such as grammars, dictionaries, and other doc…https://arxiv.org/abs/2610.36956https://arxiv.org/abs/2610.36956Tue, 27 Oct 2026 00:00:00 +0000Tamas Kovacs, Daniel Luger Handwritten text recognition (HTR) for the registers of Pope Honorius III depends on modern expert transcriptions, which exist only for regnal years 1-2 of Reg. Vat. 9. For the remaining registers, nineteenth-century scholarship offers Pressutti's regesta and partial editions. On fol. 1-203, where both layers exist, we…https://arxiv.org/abs/2610.36850https://arxiv.org/abs/2610.36850Tue, 27 Oct 2026 00:00:00 +0000Preethi S, Meeradevi T, Arun Prakash A Evaluation of Tamil handwritten character recognition (HCR) has largely focused on clean test images, and a standardized multi-corruption robustness benchmark for isolated Tamil HCR has not been established. We introduce uTHCD-C, a corruption-robustness benchmark based on the unconstrained Tamil Handwritten Character D…https://arxiv.org/abs/2610.36540https://arxiv.org/abs/2610.36540Tue, 27 Oct 2026 00:00:00 +0000Ousmane Hamadou, Jean Louis Kedieng Ebongue Fendji · BMVC 2026 Workshop Handwritten civil-status registers across Sub-Saharan Africa are being photographed, but rarely transcribed, which leaves their content unsearchable. Local administrations that hold these records typically lack GPUs, reliable connectivity, and the budget for external digitization services. We study whether a frugal pip…https://arxiv.org/abs/2611.14544https://arxiv.org/abs/2611.14544Mon, 26 Oct 2026 00:00:00 +0000Gerhard Trippen Digitizing handwritten chess scoresheets has been studied as a handwriting-recognition problem: read the move in each box and check that it is legal. This framing misses the errors that matter most. A misread that yields an illegal move is loud. One that yields a legal-but-wrong move is silent, because a legality-only …https://arxiv.org/abs/2610.35085https://arxiv.org/abs/2610.35085Mon, 26 Oct 2026 00:00:00 +0000Tamas Kovacs, Daniel Luger Unlocking medieval archives at scale is bottlenecked by the cost of line-level diplomatic transcriptions and manual abbreviation expansion. Focusing on the registers of Pope Honorius III (1216-1227) over 5,000 letters surviving in full only as manuscript images - we show that existing, line-break-free expert transcript…https://arxiv.org/abs/2611.09953https://arxiv.org/abs/2611.09953Thu, 22 Oct 2026 00:00:00 +0000Azam Nouri Handwritten character recognition systems can achieve high accuracy while retaining concentrated errors among visually similar classes. Motivated by residual letter confusions in earlier geometry-based recognition work, we investigate whether these errors can be addressed through the training objective. We introduce Co…https://arxiv.org/abs/2610.28899https://arxiv.org/abs/2610.28899Tue, 20 Oct 2026 00:00:00 +0000Elona Shatri, Jiří Mayer, Jan Hajič Synthesised data is a common strategy to address the lack of annotated handwritten music scores in Optical Music Recognition (OMR). Smashcima is a recent synthesis framework that assembles pages of handwritten scores by sampling glyph images from MUSCIMA++ and positioning them with hard-coded rules. In this work we ask…https://arxiv.org/abs/2610.27309https://arxiv.org/abs/2610.27309Mon, 19 Oct 2026 00:00:00 +0000Paraskevi Platanou, John Pavlopoulos Optical Character Recognition (OCR) and handwritten character recognition (HCR) in low-resource historical material collections often rely on data augmentation, yet augmentation strategies are typically evaluated almost exclusively through predictive accuracy. This leaves open whether a model's improved performance is …https://arxiv.org/abs/2610.25440https://arxiv.org/abs/2610.25440Sun, 18 Oct 2026 00:00:00 +0000Téa Wright, Laura X. Ma, Claudia Iron Hawk, Hannah J. Haynie, et al. Building and expanding access to digital archives supports language reclamation and creates lasting resources for community revitalization. Designing effective tools to support these efforts requires taking into account the community-, language-, and document-specific context. We investigate how small ($\leq$3B) vision…https://arxiv.org/abs/2610.24060https://arxiv.org/abs/2610.24060Fri, 16 Oct 2026 00:00:00 +0000Robin Hollifeldt, Tianru Zhang, Jessica Ström, Ekta Vats · TACL Recovering obscured text in palimpsests, i.e. the manuscripts where the original writing (under-text) was erased and overwritten for reuse, is vital for preserving lost information. Due to the nature of the material, such a task can be highly challenging and time-consuming. This paper presents an approach towards autom…https://arxiv.org/abs/2610.16956https://arxiv.org/abs/2610.16956Mon, 12 Oct 2026 00:00:00 +0000Jiu Sha, Yu Weng, Mengxiao Zhu · ACM MM 2026 Historical document analysis is crucial for cultural heritage preservation and low-resource language digitization. However, real historical documents often suffer from severe degradation, such as ink erosion and structural damage, causing mixed-visibility character sequences. Conventional OCR systems struggle in such e…https://arxiv.org/abs/2610.14531https://arxiv.org/abs/2610.14531Sat, 10 Oct 2026 00:00:00 +0000Zeyu Shan, Yongxin Shi, Shihang Wu, Peirong Zhang, et al. Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) in modern document tasks, their capabilities in Long (full-book) Historical Do…https://arxiv.org/abs/2610.12363https://arxiv.org/abs/2610.12363Thu, 08 Oct 2026 00:00:00 +0000Xiazhen Wu, Wansong Qin, Yangbin Zheng, Liangda Fang, et al. Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education. However, existing document parsing and handwriting recognition benchmarks are predominantly designed for well-structured printed documents or isolated mathematical expressions, lacking datasets that capture th…https://arxiv.org/abs/2610.33004https://arxiv.org/abs/2610.33004Wed, 07 Oct 2026 00:00:00 +0000Camilo Mariño, Diego Belzarena, Marina Gardella, Ignacio Ramírez, et al. Optical Character Recognition (OCR) is essential for digitizing historical archives, yet standard evaluation metrics such as Character Error Rate (CER) do not necessarily reflect whether meaningful information, such as names, dates, and identifiers, is preserved. In this work, we investigate entitylevel analysis of OCR…https://arxiv.org/abs/2610.01191https://arxiv.org/abs/2610.01191Thu, 01 Oct 2026 00:00:00 +0000Faias Satter, Noor Masrur, Sk. Md. Masudul Ahsan 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. The first fundamental…https://arxiv.org/abs/2610.01134https://arxiv.org/abs/2610.01134Thu, 01 Oct 2026 00:00:00 +0000Faias Satter, Sk. Md. Masudul Ahsan 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 situation is much more unus…https://arxiv.org/abs/2610.00315https://arxiv.org/abs/2610.00315Tue, 29 Sep 2026 00:00:00 +0000Ting Huang, Dongdong Wang, Mingqiu Liang, Siyang Lu 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 plausible results while fa…https://arxiv.org/abs/2609.37755https://arxiv.org/abs/2609.37755Tue, 29 Sep 2026 00:00:00 +0000Anton Repushko, Elena Chepel 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, and how accurate the…https://arxiv.org/abs/2609.37195https://arxiv.org/abs/2609.37195Tue, 29 Sep 2026 00:00:00 +0000Eric Ayllon, Abel Gandia, Jorge Calvo-Zaragoza Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down time and cost, but they also allow democratizing access and processing of their contents by generating their transcripts. However, literature in HTR currently focuses most…https://arxiv.org/abs/2609.35473https://arxiv.org/abs/2609.35473Mon, 28 Sep 2026 00:00:00 +0000Carlos Garrido-Munoz, Jorge Calvo-Zaragoza · NeurIPS 2026 In self-supervised pretraining for Handwritten Text Recognition (HTR), pixel reconstruction methods outperform contrastive methods, unlike in natural-image classification. We argue that this difference follows from where discriminative signal lies in pixel space: for HTR, it is concentrated in high-variance directions …https://arxiv.org/abs/2609.32513https://arxiv.org/abs/2609.32513Sat, 26 Sep 2026 00:00:00 +0000Paulo Grane Gabriel Silva, Lorenz Bernard Marqueses, Joel Ilao Handwritten mathematical expression recognition (HMER) refers to the task of recognizing and converting handwritten mathematics into a parsable markup language, usually LaTeX. No current state-of-the-art-competitive system adjusts to the way a specific person writes, and the domain gap between training images (usually …https://arxiv.org/abs/2609.20064https://arxiv.org/abs/2609.20064Thu, 17 Sep 2026 00:00:00 +0000Benjamin Kiessling 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 CRNN line recognizers,…https://arxiv.org/abs/2609.31681https://arxiv.org/abs/2609.31681Wed, 16 Sep 2026 00:00:00 +0000Amrit Baskota, Samyam Budhathoki, Shubham Ghimire, Abiskar Ghimire, et al. Devnagari is a one of the ancient language of the Indian subcontinent consisting of 36 vowels, 14 consonants and 10 numerals. The accurate recognition of handwritten Devnagari characters is challenging due to high complexity of Devnagari scripts. This paper presents a method to fine tune the pre trained TrOCR model to …https://arxiv.org/abs/2609.16859https://arxiv.org/abs/2609.16859Tue, 15 Sep 2026 00:00:00 +0000Manglesh Kumar Pandey, Sumit Kumar Banshal To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manually. For a script that can be read by only a small number of specialists, this manual transcription is a limitation, because the trained models are supposed to save the ti…https://arxiv.org/abs/2609.31651https://arxiv.org/abs/2609.31651Mon, 14 Sep 2026 00:00:00 +0000Nimol Thuon, Jun Du, Panhapin Theang 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 benchmark for historical pa…https://arxiv.org/abs/2609.12917https://arxiv.org/abs/2609.12917Fri, 11 Sep 2026 00:00:00 +0000Shiliang Luo Handwritten mathematical expression recognition (HMER) is conventionally scored by exact-match rates and string-similarity metrics that are blind to where an error occurs: two predictions with identical token-error counts receive identical scores whether they misplace a subscript or swap the operands of a fraction. Ren…https://arxiv.org/abs/2609.12705https://arxiv.org/abs/2609.12705Fri, 11 Sep 2026 00:00:00 +0000Dang Hoai Nam, Nguyen Duy Hieu, Quang Huu Hieu, Vo Nguyen Le Duy Handwritten text recognition resources are often small and distributed across collections that differ in language, script, document structure, and annotation format, making joint page-level training difficult. We propose ExpertHTR, a unified vision-language framework that addresses this problem through complementary su…https://arxiv.org/abs/2609.11495https://arxiv.org/abs/2609.11495Thu, 10 Sep 2026 00:00:00 +0000Yan Hon Michael Chung, Hanlin Wang Manchu, now critically endangered, was one of the principal languages of the Qing empire (1636-1912), and its extensive archival record is increasingly digitized but remains difficult to search and analyze at scale. Previous work showed that vision-language models (VLMs) trained only on synthetic Manchu word images can…https://arxiv.org/abs/2609.05662https://arxiv.org/abs/2609.05662Fri, 04 Sep 2026 00:00:00 +0000Adrian Rosello, Antonio Ríos-Vila, David Rizo, Jorge Calvo-Zaragoza Full-page end-to-end Optical Music Recognition seeks to transcribe entire music pages directly into symbolic notation, avoiding the limitations of traditional pipelines that rely on accurate staff segmentation. Recent Transformer-based architectures have achieved strong performance on typeset scores, relying on large-s…https://arxiv.org/abs/2609.03595https://arxiv.org/abs/2609.03595Thu, 03 Sep 2026 00:00:00 +0000Kunat Pipatanakul We investigate what makes synthetic OCR supervision transfer to real Thai documents and use the resulting insights to build Wayu-Paxa-OCR-Zero, a Thai OCR model adapted without OCR labels from real Thai document pages. Synthetic data provide exact labels at scale, but "realism" conflates source domain, page context, ty…https://arxiv.org/abs/2609.10572https://arxiv.org/abs/2609.10572Wed, 02 Sep 2026 00:00:00 +0000Ranjit Raut, Aarav Subedi, Ashim Shrestha Non-Latin handwritten character recognition (HCR) remains understudied. Dominant methods consider it as generic image classification, which uses model scale to implicitly learn stroke structure. Structural-prior efficiency---the principle that explicitly encoding script-geometric regularities as architectural inductive…https://arxiv.org/abs/2608.30616https://arxiv.org/abs/2608.30616Mon, 31 Aug 2026 00:00:00 +0000Gissu Valentina Naghavi, Dominik Hagmann, Martin Kampel, Irene Ballester Pottery is a primary source for reconstructing the chronological and economic dimensions of past societies. Archaeologists often document ceramic finds through technical drawings and handwritten metadata. This metadata is critical for dating, provenance attribution, and cross-site comparison, but remains inaccessible t…https://arxiv.org/abs/2608.29575https://arxiv.org/abs/2608.29575Sun, 30 Aug 2026 00:00:00 +0000Junyan Zhang, Yudong Zeng, Yongwei Huang, Zuhao Ouyang, et al. Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot control or inspect the model that performs the generation. We introduce SemTrace, a source-grounded semantic watermark for detecting whether a generated review was infl…https://arxiv.org/abs/2608.29540https://arxiv.org/abs/2608.29540Sun, 30 Aug 2026 00:00:00 +0000Mausam Gurung, Prabin Neupane, Sajjan Acharya This paper introduces NepScript Genesis, a Neural Architecture Search (NAS) framework for automated Generative Adversarial Network (GAN) discovery, applied to conditional Devanagari handwritten digit synthesis. We compare five NAS strategies against a carefully constructed Deep Convolutional GAN (DCGAN) baseline (FID=3…https://arxiv.org/abs/2608.28195https://arxiv.org/abs/2608.28195Fri, 28 Aug 2026 00:00:00 +0000Tathagata Ghosh, Sai Madhusudan Gunda, Simran Singh Sandral, Ravi Kiran Sarvadevabhatla · ICDAR 2026 Optical character recognition (OCR) for handwritten Indic manuscripts is essential for large-scale digitization and computational access to manuscript heritage. However, existing approaches are typically developed for one script at a time and require substantial script-specific customization. This limits scalability an…https://arxiv.org/abs/2608.28170https://arxiv.org/abs/2608.28170Fri, 28 Aug 2026 00:00:00 +0000Shibingfeng Zhang, Edoardo Caraffa, Annafelicia Zuffrano, Maddalena Modesti, et al. Purpose - This study investigates the feasibility of restoring missing text caused by physical lacunae in damaged ancient manuscripts using language models. Methodology - The study proposes different scenarios to replicate real-world conditions. Language models of different architectures are applied according to their …https://arxiv.org/abs/2608.27239https://arxiv.org/abs/2608.27239Thu, 27 Aug 2026 00:00:00 +0000Shahir Abdullah Rural Bangladeshi outpatient clinics keep no patient records, prescriptions are handwritten, and physicians have under a minute per patient, so they easily miss a known adverse drug reaction (ADR) or drug-drug interaction (DDI). We built and evaluated an offline Android application that photographs a handwritten prescr…