Handwriting
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EpiSAM: Character Segmentation in Challenging Stone Inscriptions
2026-06-27 10:53:13Arnav Sharma, Pratyush Jena, Amal Joseph, Ravi Kiran SarvadevabhatlaAbstract
Stone inscriptions are invaluable sources of historical and linguistic knowledge, yet their automated analysis remains a major challenge due to surface irregularities, erosion, and low visual contrast. Conventional document and handwriting analysis techniques fail to perform well in these scenarios. In this work, we propose character detection as a core strategy for robust inscription analysis. We introduce EpiSAM, a prompt-guided transformer framework for character segmentation in stone inscriptions. Rather than treating characters in isolation, EpiSAM employs a novel neighbor-aware strategy, explicitly predicting adjacent characters alongside the target. These contextual cues resolve boundary ambiguities, improving mask generation and enabling more accurate character segmentation. Furthermore, we expand an existing stone inscription dataset by adding dense polygonal annotations for characters, thereby enabling comprehensive research on Southeast Asian epigraphy. Experimental results show that EpiSAM achieves consistent improvements over existing baselines, while also exhibiting strong zero-shot generalization in challenging epigraphic scenarios.
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https://arxiv.org/abs/2606.28859
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How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations
2026-06-24 17:15:42Yuxing Cheng, Yuan Wu, Yi ChangAbstract
Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their robustness under controlled visual degradation remains insufficiently understood. This gap is critical for OCR reasoning, where visual corruption can induce OCR errors and structural distortions, thereby introducing uncertainty into the reasoning task. To systematically study this problem, we introduce OCR-Robust, a benchmark designed for evaluating OCR reasoning robustness under visual perturbations. It contains 812 samples across two complementary subsets: OCR1.0, covering documents, scene text, receipts, handwriting, and mathematical content, and OCR2.0, focusing on charts, geometry diagrams, and tables. To enable efficient yet informative evaluation, we conduct a pilot study over 18 candidate perturbations and select 5 representative types at 3 severity levels each based on their impact and cross-model discriminability. We evaluate robustness using clean accuracy, Relative Corruption Retention (RCR), Worst-Case Retention (WCR), and a composite Corruption Robustness Index (CRI), and benchmark 18 models spanning proprietary systems, open-source VLMs, and OCR+LLM pipelines. Our results show that higher clean accuracy does not necessarily imply stronger robustness, and that models can suffer pronounced degradation in the worst case on OCR tasks that are sensitive to structure, and charts and tables are substantially more fragile than document-like inputs under perturbation.
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https://arxiv.org/abs/2606.26041
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Learning Diachronic Representations of Ancient Greek Letterforms
2026-06-23 14:13:09John Pavlopoulos, Spyros Barbakos, Lavinia Ferretti, Dionysis Voulgarakis, Asimina Paparrigopoulou, Maria Konstantinidou, Giuseppe De Gregorio, Isabelle Marthot-Santaniello, Paraskevi Platanou, Holger EsslerAbstract
Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning. Taking one of the longest continuously used writing systems, ancient Greek, as a case study, we introduce three datasets for diachronic representation learning: Hell-Char, a curated training set spanning the 3rd-1st centuries BCE, and two evaluation sets, PaLit-Char (2nd-5th c. CE) and Med-Char (9th-14th c. CE). To address the challenges of symbolic variation, scarce data, and systematic degradation, we propose: a similarity-weighted supervised contrastive loss that biases embeddings using dynamically estimated inter-class similarities, and a lacuna-driven augmentation scheme that simulates realistic manuscript corruptions. Trained with these strategies, both a lightweight CNN and a pretrained ResNet achieve strong recognition performance and produce embeddings that more coherently separate character classes than PCA or generic pretrained models. These embeddings enable clustering, identification of stylistic subgroups, and construction of prototype images that visualize diachronic evolution and transitional letterforms. Our results demonstrate that respecting intrinsic inter-letter relationships and augmenting with domain-informed corruptions yield robust, interpretable representations, offering a transferable paradigm for representation learning under scarce, temporally evolving, and noisy conditions. Code and data available at: this https URL.
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https://arxiv.org/abs/2606.24984
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Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
2026-06-17 14:46:29Ramza Basharat, Muhammad Usman AliAbstract
Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scripts, research regarding Urdu Handwritten Text Recognition (UHTR) has been relatively limited. This lag of research is primarily due to the unique challenges posed by its script, and the scarcity and unavailability of benchmark datasets. Therefore, to advance research in UHTR, this study presents a specialized real dataset called the Urdu Katib Handwritten Dataset (UKHD). To the best of our knowledge, this is the first offline Urdu handwritten text lines dataset specifically curated from the materials written by Katibs in historical times. It encompasses a diverse range of flat nib writing variations in the Nastalique calligraphic style. Additionally, the effectiveness of different CRNN-based hybrid models has been evaluated to identify the optimal architecture for Urdu Katib Handwriting Recognition (UKHR). Among the analyzed models, the CNN-BGRU-CTC model showed more robust performance, with low Character Error Rate (CER) and Word Error Rate (WER). This research work aims to support and encourage the research community in developing a robust recognition system for preserving Urdu handwritten literature.
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https://arxiv.org/abs/2606.19139
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HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space
2026-06-17 08:02:19Jaward Sesay, Yue Yu, B\"orje F. KarlssonAbstract
Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings. However, these approaches are often constrained by style-specific architectural choices, heavy reliance on large datasets, high compute costs, and a lack of flexible control over writing styles through natural language. To this end, we introduce HandwritingAgent, a language-driven agent that can synthesize natural handwriting sequences directly in Scalable Vector Graphics (SVG) format with no need for style-specific training. The agent leverages a large reasoning model to geometrically analyse and autoregressively generate target handwritten glyphs as stroke sequences in a discrete grid canvas environment. Generation is conditioned on texts provided in either conversational or non-conversational mode, along with a reference handwriting-style image. Experiments on diverse handwriting tasks spanning imitation, recognition, multi-lingual handwriting synthesis, and generation of complex handwritten maths and science expressions indicate substantial improvement in performance, with HandwritingAgent matching or surpassing state-of-the-art generative handwriting models, while providing a more efficient, controllable, and generalizable synthesis method.
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https://arxiv.org/abs/2606.18788
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NIV: Neural Axis Variations for Variable Font Generation
2026-06-03 16:17:43Nadav Benedek, Ariel Shamir, Ohad FriedAbstract
Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a static font remains a labor-intensive process requiring expert typographic design and manual specification of glyph variation data. We introduce NIV (Neural Axis Variations), a method that automatically converts a static font into a fully functional variable font. Given glyph outlines and a set of desired design axes, NIV predicts per-point displacements. The model operates directly on vector glyph geometry and employs a novel Property Embedding mechanism that captures interactions between multiple axes, enabling consistent multi-axis variation within a unified framework. We train NIV on a newly constructed dataset derived from variable Google Fonts, comprising over one million variation tuples. The resulting model generalizes across unseen code points, unseen font styles, high-complexity CJK glyphs, and even out-of-distribution handwriting inputs. The generated outputs are standard variable font files supporting continuous interpolation via existing rendering engines. To facilitate research, we release the dataset, the complete training and inference implementation, and trained models at this https URL. Beyond typography, our approach demonstrates how structured geometric objects with continuous parametric variation can be synthesized using neural deformations.
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https://arxiv.org/abs/2606.05261
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Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives
2026-06-03 15:41:30Marco Peer, Thomas Gorges, Mathias Seuret, Vincent Christlein, Andreas FischerAbstract
Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual process. This paper investigates the potential of automated document analysis methods, including OCR and AI-based handwriting analysis, to support this task. We propose a pipeline combining template-based line segmentation with text recognition and writer retrieval techniques, evaluated on a dataset of 443 handwritten entries from 418 writers. Results show that OCR struggles with out-of-vocabulary handwriting, with a CER of 29.6% for first names. In contrast, writer retrieval performs more robustly, reaching an mAP of 50.6%. Furthermore, our experiments indicate that off-the-shelf OCR systems are not sufficiently reliable for transcription of handwritten signature data, particularly for short, out-of-vocabulary entries such as names or addresses. However, writer retrieval methods can effectively identify visually similar entries across signature lists, making them a suitable tool for supporting the detection of potential duplicate submissions based on handwriting similarity.
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https://arxiv.org/abs/2606.05018
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Detecting Pen-In-Air States from Video: A Proof-of-Concept Toward Complementary Handwriting Analysis
2026-06-01 14:50:27Lauren Sismeiro, Remy Plastre, Binbin Xu, Frederic Puyjarinet, Gerard DrayAbstract
Dynamic aspects of handwriting are critical for assessing developmental disorders such as dysgraphia and are typically captured using digitizing tablets. However, tablet-based sensing restricts analysis of Pen-Up behavior to a short proximity range above the writing surface, potentially missing high-lift in-air movements. As a proof of concept, we investigate whether top-view video can provide a complementary source of information for inferring pen-contact states without relying on tablet proximity sensing. We propose an interpretable hybrid pipeline combining pen-tip tracking using a YOLO-based detector with kinematic feature extraction and machine learning classification. A pilot dataset of diverse handwriting videos was manually annotated at the frame level and evaluation used a Leave-One-Video-Out (LOVO) protocol. The method achieved reliable event-level detection of Pen-Up segments, with an F_2 score up to 0.805, consistent with the emphasis on recall in a screening-oriented setting. These results support the feasibility of video-based Pen-Up detection as a low-cost and non-intrusive complement to digitizing tablets, and provide a foundation for future large-scale studies.
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https://arxiv.org/abs/2606.02342
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Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility
2026-05-26 08:18:52Ye Kyaw Thu, Thazin Myint Oo, Thepchai SupnithiAbstract
Traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units operate on discrete time steps, often failing to capture the fluid temporal dynamics of real-world physical processes. Liquid Neural Networks (LNNs), specifically Closed-form Continuous-time (CfC) networks, address this by modeling the hidden state evolution as a continuous differential equation. In this paper, we conduct a comprehensive benchmarking study across four distinct sequential modalities: neuromorphic event-based data (N-MNIST), stroke-based drawing (QuickDraw), visual handwriting (IAM), and physiological time-series (PhysioNet Sepsis-3). Furthermore, we perform a rigorous stress test using temporal dropout to evaluate model robustness against missing data. Our findings reveal that LNNs consistently provide superior parameter efficiency and significantly higher robustness in natively temporal domains and clinical environments where data sparsity is prevalent. This extended preprint provides additional background on related datasets and the LNN theoretical lineage, supplemented with a detailed appendix documenting our full implementation and experimental settings.
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https://arxiv.org/abs/2605.27467
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RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing
2026-05-25 15:30:02Sofiat Abioye, Ufaq Khan, Shazad Ashraf, Anusha Jose, Benjamin Wallace, William Poulett, Adam Byfield, Lukman Akanbi, Muhammad BilalAbstract
Urgent suspected colorectal cancer (CRC) referrals create operational bottlenecks because semi-structured clinical documents often require manual review and transcription. The original RAPTOR system used Large Language Models for structured extraction but relied on a separate OCR stage, making it vulnerable to handwriting, layout variation, and loss of visual evidence linkage. We present RAPTOR+, a multimodal extension that uses Vision-Language Models (VLMs) for end-to-end referral understanding. We evaluate fine-tuned VLMs, commercial and open-source zero-shot VLMs, and the original OCR-based pipeline on 223 clinically curated CRC urgent referral forms. We also introduce a grounding-aware evaluation framework that measures both extraction accuracy and evidence localisation. Results show a clear grounding gap in zero-shot models. Gemini 2.5 Flash achieved 92.6% Reading Accuracy but only 1.2% Strict Safety. In contrast, fine-tuned Qwen3-VL-8B achieved 96.1% Reading Accuracy and 60.6% Strict Safety, substantially improving verifiable evidence grounding. These findings show that task-specific fine-tuning is essential for reliable, auditable clinical document understanding. RAPTOR+ enables extracted referral decisions to be linked to visual evidence, supporting safer and more efficient cancer referral triage.
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https://arxiv.org/abs/2605.25956
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Threats to Arabic Handwriting Recognition: Investigating Black-Box Adversarial Attacks on embedded ConvNet models
2026-05-18 08:45:16Mohsine EL Khayati, Abdelillah Semma, Abdelaziz Courr, Rachid ElouahbiAbstract
Arabic handwriting recognition (AHR) has made significant progress with deep learning models. AHR research has largely focused on performance, with security receiving little attention. This study provides what appears to be a new line of inquiry by demonstrating the vulnerability of high-performing models to adversarial black-box attacks. The focus on black-box attacks reflects real-world scenarios where the attacker has no prior knowledge of the model architecture. Extensive experiments were conducted on two benchmark AHR datasets containing Arabic handwritten Characters. Results demonstrated the effectiveness of the attacks, with the Pixle attack achieving an attack success rate of 99-100\% on most models. Other, less aggressive attacks achieved success rates of 50-96\% across most experiments. Despite the higher attack success rate, the attacks maintain the structural integrity of the characters, rendering them almost imperceptible to the human eye. The findings indicate the higher vulnerability of the studied models to adversarial manipulation. This underscores the need to strengthen efforts to secure these models and ensure their reliability in AHR real-world applications.
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https://arxiv.org/abs/2605.18058
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From Historical Tabular Image to Knowledge Graphs: A Provenance-Aware Modular Pipeline
2026-05-06 14:33:11Sarah Binta Alam Shoilee, Victor de Boer, Jacco van Ossenbruggen, Susan Leg\^eneAbstract
Handwritten archival tables contain rich historical information, yet transforming them into structured representations, such as Knowledge Graphs, requires integrating table structure recognition, handwriting recognition, and semantic interpretation - a complex multimodal process. End-to-end AI implementations can obscure these steps, resulting in opaque algorithmic operations that hinder human oversight, critical assessment, and trust. To address this, we present a modular, provenance-aware pipeline to convert handwritten tabular images into KGs supporting human-AI collaboration. The pipeline decomposes the workflow into three stages - table reconstruction, information extraction, and KG construction - while exposing intermediate representations for inspection, evaluation, and correction. A key contribution of our approach is the systematic integration of data provenance at every stage, ensuring that all extracted entities and literals remain traceable to their visual and textual origins. The proposed pipeline is demonstrated through a number of experiments on real-world archival material concerning military careers. The results across three different table reconstruction variants highlight the importance of modularisation. By coupling modularity with data provenance, our work advances transparent and collaboratively controllable image-to-KG pipelines for complex historical data.
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https://arxiv.org/abs/2605.08222
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Cross-Domain Adversarial Augmentation: Stabilizing GANs for Medical and Handwriting Data Scarcity
2026-05-03 10:48:26Md. Sohanuzzaman Soad, Mahady Al Hady, S M Rafiuddin Rifat, Sudip GhoseAbstract
Generative Adversarial Networks (GANs) offer a pragmatic route to mitigate data scarcity in vision tasks. We study generative augmentation across two low-resource domains: Bangla handwritten characters and chest X-ray imaging using DCGAN-style models trained at 64x64 resolution. We evaluate fidelity and diversity via Inception Score (IS), Fr'echet Inception Distance (FID), and embedding visualizations (t-SNE/UMAP), and assess downstream utility by training classifiers on real versus real synthetic data. Our experiments show that generative augmentation improves sample diversity and yields consistent gains in classifier performance under limited-data regimes. We analyze stability enhancements (e.g., gradient-penalized objectives and spectral normalization) and report ablations on synthetic-to-real ratios and sample filtering. We discuss evaluation caveats for medical images, dataset licensing, and privacy risks associated with synthetic data. The resulting protocol is simple to reproduce and provides a strong baseline for applying generative augmentation to resource-constrained imaging tasks.
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https://arxiv.org/abs/2605.01815
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Democratizing the medieval English legal tradition
2026-05-01 18:00:00Michael Zhang, Elise Wang, Charlotte Whatley, Seth Strickland, Dylan BannonAbstract
The record of the beginning of the most widespread legal system in the world is contained in millions of pages of handwritten text. Most of the records of the first centuries of the Anglo-American legal system are hand-written in a highly abbreviated form of medieval Latin which only a few dozen scholars in the world are trained to read. In this interdisciplinary project, we construct a dataset of 4029 lines of text across 193 medieval criminal and civil cases. We then use the dataset to train an open-source end-to-end pipeline for transcribing these manuscripts. We first train standard neural network architectures for line segmentation and handwriting recognition (R-Blla and CNN+LSTM with CTC decoding, respectively) and show that they can already achieve 79% word accuracy, despite the relatively small training set and the challenge of expanding abbreviations. We then demonstrate that simple post-processing significantly boosts accuracy: adding an n-gram language model to the CTC decoder improves word accuracy to 82%, while asking Gemini Pro 3 to correct mistakes boosts accuracy to 88%. Finally, we compare the CNN+LSTM architecture with TrOCR, a transformer-based OCR architecture, demonstrating that TrOCR shows comparable word accuracy but worse character accuracy due to its over-willingness to guess, making it harder for humans to infer the correct reading. We incorporated our pipeline into a web portal (this http URL), opening up the English legal tradition to legal scholars, medievalists, and students.
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https://arxiv.org/abs/2605.00977
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Learning to Decipher from Pixels -- A Case Study of Copiale
2026-04-26 12:51:41Lei Kang, Giuseppe De Gregorio, Raphaela Heil, Alicia Forn\'es, Be\'ata MegyesiAbstract
Historical encrypted manuscripts require both paleographic interpretation of cipher symbols and cryptanalytic recovery of plaintext. Most existing computational workflows rely on a transcription-first paradigm, in which handwritten symbols are transcribed prior to decipherment. This intermediate step is labor-intensive, error-prone, and not always aligned with the goal of direct plaintext recovery. We propose an end-to-end, transcription-free approach that directly maps handwritten cipher images to plaintext. Using the Copiale cipher as a case study, we introduce the first text-line-level dataset pairing cipher images with German plaintext. We show that pretraining on generic handwriting data followed by cipher-specific fine-tuning substantially improves decipherment accuracy. Our results demonstrate that transcription-free image-to-plaintext decipherment is both feasible and effective for historical substitution ciphers, offering a simplified and scalable alternative to traditional pipelines. this https URL
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https://arxiv.org/abs/2604.23683
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From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms
2026-04-14 17:45:22Nicholas Pather, Joshua Fouch\'e, Sitwala Mundia, Karl-G\"unter Technau, Thokozile Malaba, Alex Welte, Ushma Mehta, Bruce A. BassettAbstract
Manual digitisation of structured handwritten documents is slow and costly. We benchmark 17 leading frontier multi-modal large language models and open-source models against a very challenging real-world medical form that mixes dates; structured, printed text; hand-written responses and significant variability challenges. None of the smaller or older models perform well but the latest Google and OpenAI models reach accuracies around $85\%$ with weighted F1 scores $\simeq 90\%$ across the discrete or predefined fields despite the very challenging nature of the responses. Clear task specific strengths emerge: GPT 5.4 excels in noisy date extraction as well as reliability with the lowest hallucination rate ($6\%$). Claude Sonnet 4.6 had the best average performance across formatted fields (dates and numerical values), while Gemini 3.1 delivered the best overall performance, with the lowest free text error rates (WER = $0.50$ and CER = $0.31$) and the strongest results across discrete classification metrics. We further show that prompt optimisation dramatically improves macro precision, recall and F1 by over $60\%$, but has little impact on weighted metrics (only $\sim2-5\%$ improvement). These results provide evidence that the rapid improvements of multimodal large language models offer a compelling pathway toward fully automated digitisation of complex handwritten workflows that is particularly relevant in low- and middle-income countries.
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https://arxiv.org/abs/2604.16504
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Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
2026-04-08 07:33:31Carlos Garrido-Munoz, Aniello Panariello, Silvia Cascianelli, Angelo Porrello, Simone Calderara, Jorge Calvo-Zaragoza, Rita CucchiaraAbstract
Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods still require real samples from the target domain. In this work, we tackle the fully zero-shot synthetic-to-real generalization setting, where no real data from the target language is available. Our approach learns how model parameters change when moving from synthetic to real handwriting in one or more source languages and transfers this learned correction to new target languages. When using multiple sources, we rely on linguistic similarity to weigh their contrubition when combining them. Experiments across five languages and six architectures show consistent improvements over synthetic-only baselines and reveal that the transferred corrections benefit even languages unrelated to the sources.
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https://arxiv.org/abs/2604.09713
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CASHG: Context-Aware Stylized Online Handwriting Generation
2026-04-02 14:32:31Jinsu Shin, Sungeun Hong, Jin Yeong BakAbstract
Online handwriting represents strokes as time-ordered trajectories, which makes handwritten content easier to transform and reuse in a wide range of applications. However, generating natural sentence-level online handwriting that faithfully reflects a writer's style remains challenging, since sentence synthesis demands context-dependent characters with stroke continuity and spacing. Prior methods treat these boundary properties as implicit outcomes of sequence modeling, which becomes unreliable at the sentence scale and under limited compositional diversity. We propose CASHG, a context-aware stylized online handwriting generator that explicitly models inter-character connectivity for style-consistent sentence-level trajectory synthesis. CASHG uses a Character Context Encoder to obtain character identity and sentence-dependent context memory and fuses them in a bigram-aware sliding-window Transformer decoder that emphasizes local predecessor--current transitions, complemented by gated context fusion for sentence-level this http URL proceeds through a three-stage curriculum from isolated glyphs to full sentences, improving robustness under sparse transition coverage. We further introduce Connectivity and Spacing Metrics (CSM), a boundary-aware evaluation suite that quantifies cursive connectivity and spacing similarity. Under benchmark-matched evaluation protocols, CASHG consistently improves CSM over comparison methods while remaining competitive in DTW-based trajectory similarity, with gains corroborated by a human evaluation.
Abstract (translated)
在线手写将笔迹表示为时间有序的轨迹，这使得手写内容在众多应用中更易于转换和复用。然而，生成能忠实反映书写者风格的、自然的句子级在线手写仍具挑战性，因为句子合成需要具备笔划连贯性和间距的、依赖上下文的字符。先前方法将这些边界特性视为序列建模的隐式结果，这在句子尺度上及组合多样性有限时变得不可靠。我们提出了CASHG，一个上下文感知的、风格化的在线手写生成器，它通过显式建模字符间连接性，来实现风格一致的句子级轨迹合成。CASHG使用一个字符上下文编码器来获取字符身份和句子依赖的上下文记忆，并在一个关注二元组的滑动窗口Transformer解码器中融合它们，该解码器强调局部前字-当前字过渡，并辅以门控上下文融合以实现句子级一致性。训练通过一个从独立字形到完整句子的三阶段渐进式方案进行，提高了稀疏过渡覆盖下的鲁棒性。我们还引入了连通与间距度量（CSM），一个边界感知的评估套件，用于量化草书连接性和间距相似性。在匹配基准的评估协议下，CASHG在CSM上持续优于对比方法，同时在基于动态时间规整的轨迹相似度上保持竞争力，这些提升也通过人工评估得到了证实。
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https://arxiv.org/abs/2604.02103
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Few-shot Writer Adaptation via Multimodal In-Context Learning
2026-03-31 08:55:11Tom Simon, Stephane Nicolas, Pierrick Tranouez, Clement Chatelain, Thierry PaquetAbstract
While state-of-the-art Handwritten Text Recognition (HTR) models perform well on standard benchmarks, they frequently struggle with writers exhibiting highly specific styles that are underrepresented in the training data. To handle unseen and atypical writers, writer adaptation techniques personalize HTR models to individual handwriting styles. Leading writer adaptation methods require either offline fine-tuning or parameter updates at inference time, both involving gradient computation and backpropagation, which increase computational costs and demand careful hyperparameter tuning. In this work, we propose a novel context-driven HTR framework3 inspired by multimodal in-context learning, enabling inference-time writer adaptation using only a few examples from the target writer without any parameter updates. We further demonstrate the impact of context length, design a compact 8M-parameter CNN-Transformer that enables few-shot in-context adaptation, and show that combining context-driven and standard OCR training strategies leads to complementary improvements. Experiments on IAM and RIMES validate our approach with Character Error Rates of 3.92% and 2.34%, respectively, surpassing all writer-independent HTR models without requiring any parameter updates at inference time.
Abstract (translated)
尽管最先进的手写文本识别（HTR）模型在标准基准测试中表现良好，但它们经常难以处理在训练数据中代表性不足的高度特定书写风格。为应对未见且非典型的书写者，书写者自适应技术能够将HTR模型个性化以适应个体书写风格。领先的书写者自适应方法需要离线微调或在推理时进行参数更新，两者都涉及梯度计算和反向传播，这会增加计算成本并需要仔细的超参数调优。在本研究中，我们提出了一种新颖的、受多模态上下文学习启发的上下文驱动HTR框架，能够在推理时仅使用目标书写者的少量示例实现书写者自适应，且无需任何参数更新。我们进一步展示了上下文长度的影响，设计了一个紧凑的8M参数CNN-Transformer模型以实现少样本上下文自适应，并证明结合上下文驱动与标准OCR训练策略能带来互补性改进。在IAM和RIMES数据集上的实验验证了我们的方法，分别达到3.92%和2.34%的字符错误率，超越了所有无需在推理时进行参数更新的书写者无关HTR模型。
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https://arxiv.org/abs/2603.29450
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Efficient Domain Adaptation for Text Line Recognition via Decoupled Language Models
2026-03-30 04:39:26Arundhathi Dev, Justin ZhanAbstract
Optical character recognition remains critical infrastructure for document digitization, yet state-of-the-art performance is often restricted to well-resourced institutions by prohibitive computational barriers. End-to-end transformer architectures achieve strong accuracy but demand hundreds of GPU hours for domain adaptation, limiting accessibility for practitioners and digital humanities scholars. We present a modular detection-and-correction framework that achieves near-SOTA accuracy with single-GPU training. Our approach decouples lightweight visual character detection (domain-agnostic) from domain-specific linguistic correction using pretrained sequence models including T5, ByT5, and BART. By training the correctors entirely on synthetic noise, we enable annotation-free domain adaptation without requiring labeled target images. Evaluating across modern clean handwriting, cursive script, and historical documents, we identify a critical "Pareto frontier" in architecture selection: T5-Base excels on modern text with standard vocabulary, whereas ByT5-Base dominates on historical documents by reconstructing archaic spellings at the byte level. Our results demonstrate that this decoupled paradigm matches end-to-end transformer accuracy while reducing compute by approximately 95%, establishing a viable, resource-efficient alternative to monolithic OCR architectures.
Abstract (translated)
光学字符识别仍是文档数字化的重要基础设施，然而最先进的性能往往因高昂的计算门槛而局限于资源充足的机构。端到端Transformer架构虽能实现高精度，但领域自适应需消耗数百GPU小时，限制了实践者与数字人文研究者的使用。我们提出一种模块化的检测-校正框架，仅用单GPU训练即可实现接近SOTA的准确率。该方法将轻量级的视觉字符检测（领域无关）与基于预训练序列模型（包括T5、ByT5和BART）的领域特定语言校正解耦。通过完全在合成噪声数据上训练校正器，我们实现了无需标注目标图像的免标注领域自适应。在现代清晰手写体、草书及历史文档的跨领域评估中，我们识别出架构选择的关键"帕累托前沿"：T5-Base模型在标准词汇的现代文本上表现卓越，而ByT5-Base则凭借字节级重建古旧拼写，在历史文档中占据优势。结果表明，这种解耦范式在匹配端到端Transformer精度的同时，将计算量降低约95%，为整体式OCR架构提供了可行且资源高效的替代方案。
URL
https://arxiv.org/abs/2603.28028
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