Handwriting
-
JaWildText: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding
2026-03-30 01:36:16Koki Maeda (Institute of Science Tokyo, Tokyo, Japan, Research and Development Center for Large Language Models, National Institute of Informatics, Tokyo, Japan), Naoaki Okazaki (Institute of Science Tokyo, Tokyo, Japan, Research and Development Center for Large Language Models, National Institute of Informatics, Tokyo, Japan)Abstract
Japanese scene text poses challenges that multilingual benchmarks often fail to capture, including mixed scripts, frequent vertical writing, and a character inventory far larger than the Latin alphabet. Although Japanese is included in several multilingual benchmarks, these resources do not adequately capture the language-specific complexities. Meanwhile, existing Japanese visual text datasets have primarily focused on scanned documents, leaving in-the-wild scene text underexplored. To fill this gap, we introduce JaWildText, a diagnostic benchmark for evaluating vision-language models (VLMs) on Japanese scene text understanding. JaWildText contains 3,241 instances from 2,961 images newly captured in Japan, with 1.12 million annotated characters spanning 3,643 unique character types. It comprises three complementary tasks that vary in visual organization, output format, and writing style: (i) Dense Scene Text Visual Question Answering (STVQA), which requires reasoning over multiple pieces of visual text evidence; (ii) Receipt Key Information Extraction (KIE), which tests layout-aware structured extraction from mobile-captured receipts; and (iii) Handwriting OCR, which evaluates page-level transcription across various media and writing directions. We evaluate 14 open-weight VLMs and find that the best model achieves an average score of 0.64 across the three tasks. Error analyses show recognition remains the dominant bottleneck, especially for kanji. JaWildText enables fine-grained, script-aware diagnosis of Japanese scene text capabilities, and will be released with evaluation code.
Abstract (translated)
日语场景文本面临着多语言基准测试常难以捕捉的挑战，包括混合文字系统、频繁的竖排书写，以及远超拉丁字母的字符总量。尽管日语已被纳入多个多语言基准测试，但这些资源未能充分体现该语言特有的复杂性。与此同时，现有日语视觉文本数据集主要聚焦扫描文档，对现实场景中的文本探索不足。为填补这一空白，我们推出JaWildText——一个用于评估视觉-语言模型（VLMs）日语场景文本理解能力的诊断基准。该数据集包含2,961张在日本新采集图像中的3,241个实例，覆盖3,643种独特字符类型，共标注112万个字符。数据集包含三个互补任务，在视觉组织、输出格式和书写风格上各有差异：（i）密集场景文本视觉问答（STVQA），需对多段视觉文本证据进行推理；（ii）收据关键信息提取（KIE），测试对手机拍摄收据的布局感知结构化提取；（iii）手写OCR，评估跨媒介和书写方向的整页转录。我们评估了14个开源视觉-语言模型，发现最佳模型在三项任务上的平均得分仅为0.64。错误分析表明，识别仍是主要瓶颈，尤其是对汉字。JaWildText支持细粒度、感知文字系统的日语场景文本能力诊断，并将随评估代码一并开源。
URL
https://arxiv.org/abs/2603.27942
PDF
-
Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math
2026-03-26 02:57:20Dingjie Song, Tianlong Xu, Yi-Fan Zhang, Hang Li, Zhiling Yan, Xing Fan, Haoyang Li, Lichao Sun, Qingsong WenAbstract
Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique challenges due to diverse handwriting, complex layouts, and varied problem-solving approaches. Existing educational NLP primarily focuses on textual responses and neglects the complexity and multimodality inherent in authentic handwritten scratchwork. Current multimodal large language models (MLLMs) excel at visual reasoning but typically adopt an "examinee perspective", prioritizing generating correct answers rather than diagnosing student errors. To bridge these gaps, we introduce ScratchMath, a novel benchmark specifically designed for explaining and classifying errors in authentic handwritten mathematics scratchwork. Our dataset comprises 1,720 mathematics samples from Chinese primary and middle school students, supporting two key tasks: Error Cause Explanation (ECE) and Error Cause Classification (ECC), with seven defined error types. The dataset is meticulously annotated through rigorous human-machine collaborative approaches involving multiple stages of expert labeling, review, and verification. We systematically evaluate 16 leading MLLMs on ScratchMath, revealing significant performance gaps relative to human experts, especially in visual recognition and logical reasoning. Proprietary models notably outperform open-source models, with large reasoning models showing strong potential for error explanation. All evaluation data and frameworks are publicly available to facilitate further research.
Abstract (translated)
评估学生手写草稿演算对个性化教育反馈至关重要，但由于字迹多样、布局复杂及解题思路各异，该任务面临独特挑战。现有教育自然语言处理研究主要聚焦文本作答，忽视了真实手写草稿演算中固有的复杂性与多模态特性。当前多模态大语言模型虽擅长视觉推理，但通常采用"应试视角"，优先生成正确答案而非诊断学生错误。为弥补这些差距，我们推出ScratchMath——首个专为解释和分类真实手写数学草稿演算错误而设计的基准数据集。该数据集包含1720份中国中小学生的数学样本，支持错误归因解释（ECE）与错误归因分类（ECC）两项核心任务，涵盖七类明确定义的错误类型。数据集通过专家标注、复核与验证的多阶段人机协作流程精心标注。我们在ScratchMath上系统评估了16个主流多模态大语言模型，发现其与人类专家存在显著性能差距，尤其在视觉识别与逻辑推理方面。专有模型普遍优于开源模型，大型推理模型在错误解释方面展现强劲潜力。所有评估数据与框架均已公开，以推动后续研究。
URL
https://arxiv.org/abs/2603.24961
PDF
-
Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
2026-03-24 07:57:26Chen Zheng, Yuxuan Lai, Haoyang Lu, Wentao Ma, Jitao Yang, Jian WangAbstract
The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills. In this paper, we leverage vision-language models (VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1) and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowledge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwritten Chinese character quality.
Abstract (translated)
汉字书写是学习汉语的基础。以往自动化评估方法常将评分定义为回归问题，但仅提供分数的反馈缺乏可操作的指导，限制了其在提升书写技能方面的效果。本文利用视觉-语言模型（VLMs）分析手写汉字质量并生成多层级反馈。具体而言，我们研究了两种反馈生成任务：简单等级反馈（任务1）和丰富的描述性反馈（任务2）。我们探索了基于低秩适应（LoRA）的微调策略与上下文学习方法，以将审美评估知识融入VLMs。实验结果表明，该方法在CCL 2025手写汉字质量评估工作坊的多项评测赛道中达到最先进水平。
URL
https://arxiv.org/abs/2603.26768
PDF
-
Prediction of Grade, Gender, and Academic Performance of Children and Teenagers from Handwriting Using the Sigma-Lognormal Model
2026-03-12 04:05:23Adrian Iste, Kazuki Nishizawa, Chisa Tanaka, Andrew Vargo, Anna Scius-Bertrand, Andreas Fischer, Koichi KiseAbstract
Digital handwriting acquisition enables the capture of detailed temporal and kinematic signals reflecting the motor processes underlying writing behavior. While handwriting analysis has been extensively explored in clinical or adult populations, its potential for studying developmental and educational characteristics in children remains less investigated. In this work, we examine whether handwriting dynamics encode information related to student characteristics using a large-scale online dataset collected from Japanese students from elementary school to junior high school. We systematically compare three families of handwriting-derived features: basic statistical descriptors of kinematic signals, entropy-based measures of variability, and parameters obtained from the sigma-lognormal model. Although the dataset contains dense stroke-level recordings, features are aggregated at the student level to enable a controlled comparison between representations. These features are evaluated across three prediction tasks: grade prediction, gender classification, and academic performance classification, using Linear or Logistic Regression and Random Forest models under consistent experimental settings. The results show that handwriting dynamics contain measurable signals related to developmental stage and individual differences, especially for the grade prediction task. These findings highlight the potential of kinematic handwriting analysis and confirm that through their development, children's handwriting evolves toward a lognormal motor organization.
Abstract (translated)
数字手写采集使得能够捕捉到反映书写行为背后运动过程的详细时间与动力学信号。尽管在临床或成人人群中，手写分析已经被广泛研究，但它在儿童发展和教育特征研究中的潜力尚未得到充分探索。在这项工作中，我们考察了学生的特性信息是否通过手写动态编码，并使用了一套大规模在线数据集，该数据来自日本从小学到初中阶段的学生。我们系统地比较了三种类型的手写衍生特征：运动信号的基本统计描述符、基于熵的可变性度量以及从sigma-对数正态模型中获得的参数。 尽管数据集中包含密集的笔画级记录，但为了实现表示之间的可控对比，这些特征是在学生级别上进行聚合。使用一致的实验设置，在线性和逻辑回归及随机森林模型下评估了这三个预测任务：年级预测、性别分类以及学业成绩分类。 研究结果表明，手写动态中包含了与发展阶段和个体差异相关的可测量信号，特别是在年级预测任务中尤为明显。这些发现突出了运动学手写分析的潜力，并确认了通过发展过程，儿童的手写字迹向着对数正态运动组织进化这一事实。
URL
https://arxiv.org/abs/2603.11519
PDF
-
From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features
2026-03-12 03:59:09Chisa Tanaka, Andrew Vargo, Anna Scius-Bertrand, Andreas Fischer, Koichi KiseAbstract
While handwriting has traditionally been studied for character recognition and disease classification, its potential to reflect day-to-day physiological fluctuations in healthy individuals remains unexplored. This study examines whether daily variations in sleep-related recovery states can be inferred from online handwriting dynamics. % We propose a personalized binary classification framework that detects low-recovery days using features derived from the Sigma-Lognormal model, which captures the neuromotor generation process of pen strokes. In a 28-day in-the-wild study involving 13 university students, handwriting was recorded three times daily, and nocturnal cardiac indicators were measured using a wearable ring. For each participant, the lowest (or highest) quartile of four sleep-related metrics -- HRV, lowest heart rate, average heart rate, and total sleep duration -- defined the positive class. Leave-One-Day-Out cross-validation showed that PR-AUC significantly exceeded the baseline (0.25) for all four variables after FDR correction, with the strongest performance observed for cardiac-related variables. Importantly, classification performance did not differ significantly across task types or recording timings, indicating that recovery-related signals are embedded in general movement dynamics. These results demonstrate that subtle within-person autonomic recovery fluctuations can be detected from everyday handwriting, opening a new direction for non-invasive, device-independent health monitoring.
Abstract (translated)
虽然手写通常被研究用于字符识别和疾病分类，但它在反映健康个体日常生理波动方面的潜力尚未得到探索。本研究探讨了是否可以通过在线书写动态来推断与睡眠相关的恢复状态的日常变化。 在这项为期28天的野外研究中，13名大学学生每天记录三次手写，并通过可穿戴戒指测量夜间心脏指标。每位参与者的心率变异（HRV）、最低心率、平均心率和总睡眠时间这四个睡眠相关指标中的最低（或最高）四分位数定义了阳性类别。 在留一法交叉验证中，经过FDR校正后，所有四个变量的PR-AUC值显著高于基线（0.25），其中心脏相关的变量表现最强。值得注意的是，不同任务类型和记录时间的分类性能差异不大，表明与恢复相关的信号嵌入了一般的运动动态之中。 这些结果证明了可以通过日常书写检测到个体内部微妙的自主神经系统恢复波动，为非侵入性、设备独立性的健康监测开辟了一个新方向。
URL
https://arxiv.org/abs/2603.11512
PDF
-
CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization
2026-03-08 09:06:13Anh-Duy Le, Van-Linh Pham, Thanh-Nam Vo, Xuan Toan Mai, Tuan-Anh TranAbstract
One-shot styled handwriting image generation, despite achieving impressive results in recent years, remains challenging due to the difficulty in capturing the intricate and diverse characteristics of human handwriting by using solely a single reference image. Existing methods still struggle to generate visually appealing and realistic handwritten images and adapt to complex, unseen writer styles, struggling to isolate invariant style features (e.g., slant, stroke width, curvature) while ignoring irrelevant noise. To tackle this problem, we introduce Patch Contrastive Enhancement and Style-Aware Quantization via Denoising Diffusion (CONSTANT), a novel one-shot handwriting generation via diffusion model. CONSTANT leverages three key innovations: 1) a Style-Aware Quantization (SAQ) module that models style as discrete visual tokens capturing distinct concepts; 2) a contrastive objective to ensure these tokens are well-separated and meaningful in the embedding style space; 3) a latent patch-based contrastive (LLatentPCE) objective help improving quality and local structures by aligning multiscale spatial patches of generated and real features in latent space. Extensive experiments and analysis on benchmark datasets from multiple languages, including English, Chinese, and our proposed ViHTGen dataset for Vietnamese, demonstrate the superiority of adapting to new reference styles and producing highly detailed images of our method over state-of-the-art approaches. Code is available at GitHub
Abstract (translated)
近年来，一次性风格化手写图像生成虽然取得了令人瞩目的成果，但仍然面临着挑战。由于仅使用单一参考图像难以捕捉人类书写中细微而多样的特征，现有的方法在生成视觉上吸引人且逼真的手写图像以及适应复杂的、未见过的写作风格方面仍存在困难。它们难以分离不变的风格特征（如倾斜度、笔画宽度和曲线），同时忽略无关的噪声。 为了应对这一问题，我们提出了一种名为“Patch Contrastive Enhancement and Style-Aware Quantization via Denoising Diffusion”(CONSTANT)的新颖一次性手写生成方法，该方法基于扩散模型。CONSTANT采用三个关键创新： 1. **风格感知量化（SAQ）模块**：通过捕捉不同概念的离散视觉令牌来建模风格。 2. 对比性目标确保这些令牌在嵌入式风格空间中彼此分离且有意义。 3. 基于潜在补丁对比的目标 (LLatentPCE)，通过在潜空间中对齐生成和真实特征的多尺度空间补丁，提高图像质量和局部结构。 我们在多个语言的数据集上进行了广泛的实验和分析，包括英语、中文以及我们提出的越南语ViHTGen数据集。这些实验证明了我们的方法在适应新参考风格和产生高质量详细图像方面优于现有的最佳方法。代码可在GitHub上获取。
URL
https://arxiv.org/abs/2603.07543
PDF
-
DISCO: Document Intelligence Suite for COmparative Evaluation
2026-03-04 14:47:34Kenza Benkirane, Dan Goldwater, Martin Asenov, Aneiss GhodsiAbstract
Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types, including handwritten text, multilingual scripts, medical forms, infographics, and multi-page documents. Our evaluation shows that performance varies substantially across tasks and document characteristics, underscoring the need for complexity-aware approach selection. OCR pipelines are generally more reliable for handwriting and for long or multi-page documents, where explicit text grounding supports text-heavy reasoning, while VLMs perform better on multilingual text and visually rich layouts. Task-aware prompting yields mixed effects, improving performance on some document types while degrading it on others. These findings provide empirical guidance for selecting document processing strategies based on document structure and reasoning demands.
Abstract (translated)
文档智能需要精确的文本提取和对文档内容的可靠推理。我们推出了\textbf{DISCO}（\emph{面向对比评估的文档智能套件}），该工具分别评估光学字符识别（OCR）流水线和视觉-语言模型（VLMs）在多样化文档类型上的解析与问答能力，涵盖手写文本、多语言文本、医疗表格、信息图表及多页文档。评估结果显示，性能因任务和文档特征差异显著，凸显了选择复杂度感知方法的必要性。OCR流水线在手写内容及长文档或多页文档中通常更可靠，其中显式文本锚定支持文本密集型推理；而VLMs在多语言文本和视觉丰富布局上表现更优。任务感知提示产生混合效果：虽提升部分文档类型性能，却在另一些类型中导致下降。这些发现为基于文档结构与推理需求选择文档处理策略提供了实证指导。
URL
https://arxiv.org/abs/2603.23511
PDF
-
Tokenization vs. Augmentation: A Systematic Study of Writer Variance in IMU-Based Online Handwriting Recognition
2026-02-25 09:26:19Jindong Li, Dario Zanca, Vincent Christlein, Tim Hamann, Jens Barth, Peter K\"ampf, Bj\"orn EskofierAbstract
Inertial measurement unit-based online handwriting recognition enables the recognition of input signals collected across different writing surfaces but remains challenged by uneven character distributions and inter-writer variability. In this work, we systematically investigate two strategies to address these issues: sub-word tokenization and concatenation-based data augmentation. Our experiments on the OnHW-Words500 dataset reveal a clear dichotomy between handling inter-writer and intra-writer variance. On the writer-independent split, structural abstraction via Bigram tokenization significantly improves performance to unseen writing styles, reducing the word error rate (WER) from 15.40% to 12.99%. In contrast, on the writer-dependent split, tokenization degrades performance due to vocabulary distribution shifts between the training and validation sets. Instead, our proposed concatenation-based data augmentation acts as a powerful regularizer, reducing the character error rate by 34.5% and the WER by 25.4%. Further analysis shows that short, low-level tokens benefit model performance and that concatenation-based data augmentation performance gain surpasses those achieved by proportionally extended training. These findings reveal a clear variance-dependent effect: sub-word tokenization primarily mitigates inter-writer stylistic variability, whereas concatenation-based data augmentation effectively compensates for intra-writer distributional sparsity.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16883
PDF
-
DohaScript: A Large-Scale Multi-Writer Dataset for Continuous Handwritten Hindi Text
2026-02-20 09:25:14Kunwar Arpit Singh, Ankush Prakash, Haroon R LoneAbstract
Despite having hundreds of millions of speakers, handwritten Devanagari text remains severely underrepresented in publicly available benchmark datasets. Existing resources are limited in scale, focus primarily on isolated characters or short words, and lack controlled lexical content and writer level diversity, which restricts their utility for modern data driven handwriting analysis. As a result, they fail to capture the continuous, fused, and structurally complex nature of Devanagari handwriting, where characters are connected through a shared shirorekha (horizontal headline) and exhibit rich ligature formations. We introduce DohaScript, a large scale, multi writer dataset of handwritten Hindi text collected from 531 unique contributors. The dataset is designed as a parallel stylistic corpus, in which all writers transcribe the same fixed set of six traditional Hindi dohas (couplets). This controlled design enables systematic analysis of writer specific variation independent of linguistic content, and supports tasks such as handwriting recognition, writer identification, style analysis, and generative modeling. The dataset is accompanied by non identifiable demographic metadata, rigorous quality curation based on objective sharpness and resolution criteria, and page level layout difficulty annotations that facilitate stratified benchmarking. Baseline experiments demonstrate clear quality separation and strong generalization to unseen writers, highlighting the dataset's reliability and practical value. DohaScript is intended to serve as a standardized and reproducible benchmark for advancing research on continuous handwritten Devanagari text in low resource script settings.
Abstract (translated)
尽管有数亿使用者，手写的德瓦纳加里文本在公开可用的基准数据集中仍然严重不足。现有的资源规模有限，主要集中在孤立字符或短词上，并且缺乏控制词汇内容和书写者多样性，这限制了它们在现代基于数据的手写分析中的实用性。因此，这些数据集无法捕捉到德瓦纳加里手写的连续性、融合性和结构复杂性的特点，在这种文本中，字符通过共享的水平标题（shirorekha）连接，并且呈现出丰富的连字形式。 我们引入了DohaScript，这是一个大规模、多书写者的手写印地文数据集，由531位独特的贡献者提供。该数据集被设计为平行风格语料库，在其中所有书写者都会转录六组传统的印地文双行诗（dohas）。这种受控的设计能够系统分析独立于语言内容的特定书写者的变异性，并支持诸如手写识别、书写者鉴别、风格分析和生成模型等任务。该数据集还附带了不可识别的人口统计数据，基于客观清晰度和分辨率标准进行严格的质量筛选，并提供了页面级别的布局难度注释，以促进分层基准测试。 基线实验显示出了明显的质量差异，并且在未见过的书写者上具有强大的泛化能力，突显了该数据集的可靠性和实际价值。DohaScript旨在作为低资源脚本环境中推进连续手写德瓦纳加里文本研究的标准和可重复基准。
URL
https://arxiv.org/abs/2602.18089
PDF
-
Benchmarking Vision-Language Models for French PDF-to-Markdown Conversion
2026-02-12 13:55:43Bruno Rigal, Victor Dupriez, Alexis Mignon, Ronan Le Hy, Nicolas MeryAbstract
This report evaluates PDF-to-Markdown conversion using recent Vision-Language Models (VLMs) on challenging French documents. Document parsing is a critical step for Retrieval-Augmented Generation (RAG) pipelines, where transcription and layout errors propagate to downstream retrieval and grounding. Existing benchmarks often emphasize English or Chinese and can over-penalize benign formatting and linearization choices (e.g., line breaks, list segmentation, alternative table renderings) that are largely irrelevant for downstream use. We introduce a French-focused benchmark of difficult pages selected via model-disagreement sampling from a corpus of 60{,}000 documents, covering handwritten forms, complex layouts, dense tables, and graphics-rich pages. Evaluation is performed with unit-test-style checks that target concrete failure modes (text presence, reading order, and local table constraints) combined with category-specific normalization designed to discount presentation-only variance. Across 15 models, we observe substantially higher robustness for the strongest proprietary models on handwriting and forms, while several open-weights systems remain competitive on standard printed layouts.
Abstract (translated)
该报告评估了使用近期的视觉语言模型（VLMs）对具有挑战性的法语文档进行PDF到Markdown转换的效果。文档解析是检索增强生成（RAG）管道中的关键步骤，其中转录和布局错误会传播到下游的检索和定位任务中。现有的基准测试通常更侧重于英语或中文，并且往往会过度惩罚那些对于下游使用实际上无关紧要但与格式化选择相关的良性情况（例如，换行符、列表分割以及表格的不同呈现方式）。为此，我们引入了一个以法语为中心的新基准，该基准包含从60,000份文档中通过模型意见分歧采样得到的困难页面样本。这些样本涵盖了手写表单、复杂布局、密集表格和图形丰富的页面等不同情况。 评估是使用类似单元测试的方法进行的，包括针对具体失败模式（如文本存在性、阅读顺序以及局部表格约束）的检查，并结合了根据类别定制的标准化处理方法来消除仅与呈现有关的变化。在15种模型中，我们观察到最强劲的专有模型在手写和表单解析方面表现出显著更高的稳健性，而几种开源权重系统则在标准印刷布局上依然保持竞争力。
URL
https://arxiv.org/abs/2602.11960
PDF
-
Enhancing IMU-Based Online Handwriting Recognition via Contrastive Learning with Zero Inference Overhead
2026-02-04 13:44:54Jindong Li, Dario Zanca, Vincent Christlein, Tim Hamann, Jens Barth, Peter K\"ampf, Bj\"orn EskofierAbstract
Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardware improves privacy and lowers latency, but entails memory constraints. To address this, we propose Error-enhanced Contrastive Handwriting Recognition (ECHWR), a training framework designed to improve feature representation and recognition accuracy without increasing inference costs. ECHWR utilizes a temporary auxiliary branch that aligns sensor signals with semantic text embeddings during the training phase. This alignment is maintained through a dual contrastive objective: an in-batch contrastive loss for general modality alignment and a novel error-based contrastive loss that distinguishes between correct signals and synthetic hard negatives. The auxiliary branch is discarded after training, which allows the deployed model to keep its original, efficient architecture. Evaluations on the OnHW-Words500 dataset show that ECHWR significantly outperforms state-of-the-art baselines, reducing character error rates by up to 7.4% on the writer-independent split and 10.4% on the writer-dependent split. Finally, although our ablation studies indicate that solving specific challenges require specific architectural and objective configurations, error-based contrastive loss shows its effectiveness for handling unseen writing styles.
Abstract (translated)
使用惯性测量单元（IMU）进行在线手写识别，使纸质书写成为数字设备的输入方式。在边缘硬件上执行此操作可以提高隐私保护并降低延迟，但会导致内存限制。为了解决这个问题，我们提出了一种名为错误增强对比式手写识别（ECHWR）的训练框架，该框架旨在改进特征表示和识别准确性而不增加推理成本。ECHWR利用一个临时辅助分支，在训练阶段将传感器信号与语义文本嵌入对齐。这种对齐通过双重对比目标来维持：一种是批内对比损失，用于一般模式对齐；另一种则是基于错误的新型对比损失，用于区分正确信号和合成难例（hard negatives）。在完成训练后，辅助分支被丢弃，从而使部署模型能够保持其原始高效架构。在OnHW-Words500数据集上的评估显示，ECHWR显著优于最先进的基线方法，在不依赖书写者的情况下字符错误率降低了高达7.4%，而在依赖书写者的情况下则降低了10.4%。最后，虽然我们的消融研究（ablation studies）表明解决特定挑战需要特定的架构和目标配置，但基于错误的对比损失证明了其处理未见过书写风格的有效性。
URL
https://arxiv.org/abs/2602.07049
PDF
-
Symbol-Aware Reasoning with Masked Discrete Diffusion for Handwritten Mathematical Expression Recognition
2026-02-03 10:46:49Takaya Kawakatsu, Ryo IshiyamaAbstract
Handwritten Mathematical Expression Recognition (HMER) requires reasoning over diverse symbols and 2D structural layouts, yet autoregressive models struggle with exposure bias and syntactic inconsistency. We present a discrete diffusion framework that reformulates HMER as iterative symbolic refinement instead of sequential generation. Through multi-step remasking, the proposal progressively refines both symbols and structural relations, removing causal dependencies and improving structural consistency. A symbol-aware tokenization and Random-Masking Mutual Learning further enhance syntactic alignment and robustness to handwriting diversity. On the MathWriting benchmark, the proposal achieves 5.56\% CER and 60.42\% EM, outperforming strong Transformer and commercial baselines. Consistent gains on CROHME 2014--2023 demonstrate that discrete diffusion provides a new paradigm for structure-aware visual recognition beyond generative modeling.
Abstract (translated)
手写数学表达式识别（HMER）需要对各种符号和二维结构布局进行推理，然而自回归模型在处理曝光偏差和语法不一致性时存在困难。我们提出了一种离散扩散框架，将HMER重新表述为迭代符号细化过程，而不是顺序生成过程。通过多步重掩码技术，该方法逐步优化符号及其结构关系，消除了因果依赖性，并提高了结构的一致性。符号感知的标记化和随机掩码互学习进一步增强了语法对齐能力，并提升了对手写多样性鲁棒性的表现。在MathWriting基准测试中，我们的方案实现了5.56% 的字符错误率（CER）和60.42% 的精确匹配度（EM），超越了强大的Transformer模型及商业基线模型的性能。在CROHME 2014-2023数据集上的持续改进表明，离散扩散为结构感知型视觉识别提供了一种新的范式，超越了传统的生成建模方法。 这段描述详细介绍了如何通过离散扩散框架来解决手写数学表达式的识别问题，并强调这种方法在处理符号语法一致性和结构布局方面优于传统自回归模型。此外，文中提到的实验结果表明该技术在现有基准测试中表现出色，为未来的视觉识别任务提供了新的研究方向。
URL
https://arxiv.org/abs/2602.03370
PDF
-
A 30-item Test for Assessing Chinese Character Amnesia in Child Handwriters
2026-01-31 02:35:25Zebo Xu, Steven Langsford, Zhuang Qiu, Zhenguang CaiAbstract
Handwriting literacy is an important skill for learning and communication in school-age children. In the digital age, handwriting has been largely replaced by typing, leading to a decline in handwriting proficiency, particularly in non-alphabetic writing systems. Among children learning Chinese, a growing number have reported experiencing character amnesia: difficulty in correctly handwriting a character despite being able to recognize it. Given that there is currently no standardized diagnostic tool for assessing character amnesia in children, we developed an assessment to measure Chinese character amnesia in Mandarin-speaking school-age population. We utilised a large-scale handwriting dataset in which 40 children handwrote 800 characters from dictation prompts. Character amnesia and correct handwriting responses were analysed using a two-parameter Item Response Theory model. Four item-selection schemes were compared: random baseline, maximum discrimination, diverse difficulty, and an upper-and-lower-thirds discrimination score. Candidate item subsets were evaluated using out-of-sample prediction. Among these selection schemes, the upper-and-lower-thirds discrimination procedure yields a compact 30-item test that preserves individual-difference structure and generalizes to unseen test-takers (cross-validated mean r =.74 with full 800-item-test; within-sample r =.93). This short-form test provides a reliable and efficient tool of assessing Chinese character amnesia in children and can be used to identify early handwriting and orthographic learning difficulties, contributing to the early detection of developmental dysgraphia and related literacy challenges.
Abstract (translated)
URL
https://arxiv.org/abs/2602.00464
PDF
-
Telling Human and Machine Handwriting Apart
2026-01-16 18:45:16Luis A. Leiva, Moises Diaz, Nuwan T. Attygalle, Miguel A. Ferrer, Rejean PlamondonAbstract
Handwriting movements can be leveraged as a unique form of behavioral biometrics, to verify whether a real user is operating a device or application. This task can be framed as a reverse Turing test in which a computer has to detect if an input instance has been generated by a human or artificially. To tackle this task, we study ten public datasets of handwritten symbols (isolated characters, digits, gestures, pointing traces, and signatures) that are artificially reproduced using seven different synthesizers, including, among others, the Kinematic Theory (Sigma h model), generative adversarial networks, Transformers, and Diffusion models. We train a shallow recurrent neural network that achieves excellent performance (98.3 percent Area Under the ROC Curve (AUC) score and 1.4 percent equal error rate on average across all synthesizers and datasets) using nonfeaturized trajectory data as input. In few-shot settings, we show that our classifier achieves such an excellent performance when trained on just 10 percent of the data, as evaluated on the remaining 90% of the data as a test set. We further challenge our classifier in out-of-domain settings, and observe very competitive results as well. Our work has implications for computerized systems that need to verify human presence, and adds an additional layer of security to keep attackers at bay.
Abstract (translated)
手写动作可以被用作一种独特的行为生物识别方式，以验证实际用户是否在操作设备或应用程序。这一任务可以被视为一个反向图灵测试，在这种测试中，计算机需要检测输入实例是由真人生成的还是人工合成的。为了解决这个问题，我们研究了十个公开的手写符号数据集（包括孤立字符、数字、手势、指点轨迹和签名），这些数据集使用七种不同的生成器进行了人工再现，其中包括动学理论（Sigma h 模型）、生成对抗网络、Transformer 和扩散模型等。 我们训练了一个浅层循环神经网络，在不进行特征化处理的情况下以轨迹数据为输入，该网络在所有合成器和数据集中实现了卓越的性能（98.3% 的接收者操作特性曲线（ROC）面积得分和1.4% 的平均错误接受率）。此外，我们在少量样本设置下展示了当训练集仅为总数据量的10%，而剩余90%的数据作为测试集时，我们的分类器也能实现如此卓越的表现。我们进一步在跨域场景中挑战了这一分类器，并观察到了非常具有竞争力的结果。 这项工作对需要验证人类存在的计算机系统有着重要的影响，并为防止攻击者侵入添加了一层额外的安全保障。
URL
https://arxiv.org/abs/2601.11700
PDF
-
Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval
2026-01-16 12:55:41Fangke Chen, Tianhao Dong, Sirry Chen, Guobin Zhang, Yishu Zhang, Yining ChenAbstract
Handwritten word retrieval is vital for digital archives but remains challenging due to large handwriting variability and cross-lingual semantic gaps. While large vision-language models offer potential solutions, their prohibitive computational costs hinder practical edge deployment. To address this, we propose a lightweight asymmetric dual-encoder framework that learns unified, style-invariant visual embeddings. By jointly optimizing instance-level alignment and class-level semantic consistency, our approach anchors visual embeddings to language-agnostic semantic prototypes, enforcing invariance across scripts and writing styles. Experiments show that our method outperforms 28 baselines and achieves state-of-the-art accuracy on within-language retrieval benchmarks. We further conduct explicit cross-lingual retrieval, where the query language differs from the target language, to validate the effectiveness of the learned cross-lingual representations. Achieving strong performance with only a fraction of the parameters required by existing models, our framework enables accurate and resource-efficient cross-script handwriting retrieval.
Abstract (translated)
手写词检索对于数字档案至关重要，但由于书写风格的多样性以及跨语言语义差距的存在，这一任务依然具有挑战性。虽然大型视觉-语言模型提供了潜在解决方案，但其高昂的计算成本阻碍了在实际边缘设备上的部署。为了解决这个问题，我们提出了一种轻量级的不对称双编码器框架，该框架能够学习统一且风格不变的视觉嵌入。通过同时优化实例级别的对齐和类级别语义一致性，我们的方法将视觉嵌入固定到语言无关的语义原型上，并确保跨书写体系和风格的一致性。实验表明，与28个基准模型相比，我们提出的方法在同语言检索基准测试中实现了最先进的准确率。此外，我们在查询语言不同于目标语言的情况下进行了显式的跨语言检索，以验证学习得到的跨语言表示的有效性。通过使用现有模型所需参数的极小部分就能实现强大的性能，我们的框架能够实现在资源有限条件下的精确和高效的多书写体系手写检索。
URL
https://arxiv.org/abs/2601.11248
PDF
-
Show, don't tell -- Providing Visual Error Feedback for Handwritten Documents
Abstract
Handwriting remains an essential skill, particularly in education. Therefore, providing visual feedback on handwritten documents is an important but understudied area. We outline the many challenges when going from an image of handwritten input to correctly placed informative error feedback. We empirically compare modular and end-to-end systems and find that both approaches currently do not achieve acceptable overall quality. We identify the major challenges and outline an agenda for future research.
Abstract (translated)
手写仍然是一个重要的技能，特别是在教育领域。因此，在手写文档中提供视觉反馈是一个重要但研究不足的领域。我们概述了从手写输入图像到正确放置的信息性错误反馈之间存在的诸多挑战。通过实证分析模块化系统和端到端系统的差异，我们发现目前这两种方法都无法达到令人满意的总体质量水平。我们识别出了主要的挑战，并为未来的研究制定了议程。
URL
https://arxiv.org/abs/2601.09586
PDF
-
Character Detection using YOLO for Writer Identification in multiple Medieval books
2026-01-08 11:11:24Alessandra Scotto di Freca, Tiziana D Alessandro, Francesco Fontanella, Filippo Sarria, Claudio De StefanoAbstract
Paleography is the study of ancient and historical handwriting, its key objectives include the dating of manuscripts and understanding the evolution of writing. Estimating when a document was written and tracing the development of scripts and writing styles can be aided by identifying the individual scribes who contributed to a medieval manuscript. Although digital technologies have made significant progress in this field, the general problem remains unsolved and continues to pose open challenges. ... We previously proposed an approach focused on identifying specific letters or abbreviations that characterize each writer. In that study, we considered the letter "a", as it was widely present on all pages of text and highly distinctive, according to the suggestions of expert paleographers. We used template matching techniques to detect the occurrences of the character "a" on each page and the convolutional neural network (CNN) to attribute each instance to the correct scribe. Moving from the interesting results achieved from this previous system and being aware of the limitations of the template matching technique, which requires an appropriate threshold to work, we decided to experiment in the same framework with the use of the YOLO object detection model to identify the scribe who contributed to the writing of different medieval books. We considered the fifth version of YOLO to implement the YOLO object detection model, which completely substituted the template matching and CNN used in the previous work. The experimental results demonstrate that YOLO effectively extracts a greater number of letters considered, leading to a more accurate second-stage classification. Furthermore, the YOLO confidence score provides a foundation for developing a system that applies a rejection threshold, enabling reliable writer identification even in unseen manuscripts.
Abstract (translated)
古文字学是研究古代和历史上的手写笔迹的学科，其主要目标包括对抄本进行年代鉴定以及理解书写的发展演变。通过识别贡献于中世纪手稿的个人抄写员，可以辅助估算文档的写作时间和追踪脚本及书写风格的变化发展。尽管数字技术在这一领域取得了显著进展，但总体问题仍未解决，并且仍然面临诸多挑战。 我们之前提出了一种专注于识别每个作者特征特定字母或缩写的的方法。在这项研究中，我们考虑了字母“a”，因为它广泛存在于所有文本页面上并且根据古文字学专家的建议具有高度辨识度。我们使用模板匹配技术来检测每一页上的字符"a"出现情况，并利用卷积神经网络（CNN）将每个实例归因于正确的抄写员。 基于之前系统取得的有趣成果以及意识到模板匹配技术需要适当阈值才能工作的限制，我们决定在同一框架下实验采用YOLO目标检测模型来识别不同中世纪书籍中的作者。我们考虑使用YOLO的第五版本来实施YOLO对象检测模型，这完全取代了先前工作中使用的模板匹配和CNN。 实验结果表明，YOLO能够有效地提取更多被考量的字母数量，从而实现更精确的第二阶段分类。此外，YOLO置信度分数为开发应用拒绝阈值系统的奠定了基础，使得在未见过的手稿中进行可靠的作者识别也成为可能。
URL
https://arxiv.org/abs/2601.04834
PDF
-
Analyzing the Structure of Handwritten Digits: A Comparative Study of PCA, Factor Analysis, and UMAP
2026-01-07 06:20:37Jyotiraditya GuptaAbstract
Handwritten digit images lie in a high-dimensional pixel space but exhibit strong geometric and statistical structure. This paper investigates the latent organization of handwritten digits in the MNIST dataset using three complementary dimensionality reduction techniques: Principal Component Analysis (PCA), Factor Analysis (FA), and Uniform Manifold Approximation and Projection (UMAP). Rather than focusing on classification accuracy, we study how each method characterizes intrinsic dimensionality, shared variation, and nonlinear geometry. PCA reveals dominant global variance directions and enables high-fidelity reconstructions using a small number of components. FA decomposes digits into interpretable latent handwriting primitives corresponding to strokes, loops, and symmetry. UMAP uncovers nonlinear manifolds that reflect smooth stylistic transitions between digit classes. Together, these results demonstrate that handwritten digits occupy a structured low-dimensional manifold and that different statistical frameworks expose complementary aspects of this structure.
Abstract (translated)
手写数字图像存在于高维像素空间中，但表现出强烈的几何和统计结构。本文使用三种互补的降维技术：主成分分析（PCA）、因子分析（FA）以及均匀流形近似与投影（UMAP），来研究MNIST数据集中的手写数字的潜在组织情况。不同于关注分类准确度，我们探讨每种方法如何表征固有维度、共同变化和非线性几何。 PCA揭示了主导全局方差的方向，并能利用少量组件实现高保真重构。FA将数字分解为可解释的手部书写基本元素，这些元素对应于笔画、环形和对称结构。UMAP则发现了反映不同数字类别之间平滑风格转换的非线性流形。 这些结果共同表明手写数字占据一个结构化的低维流形，并且不同的统计框架揭示了该结构的不同方面。
URL
https://arxiv.org/abs/2601.06168
PDF
-
Grading Handwritten Engineering Exams with Multimodal Large Language Models
2026-01-02 16:10:08Janez Per\v{s}, Jon Muhovi\v{c}, Andrej Ko\v{s}ir, Bo\v{s}tjan MurovecAbstract
Handwritten STEM exams capture open-ended reasoning and diagrams, but manual grading is slow and difficult to scale. We present an end-to-end workflow for grading scanned handwritten engineering quizzes with multimodal large language models (LLMs) that preserves the standard exam process (A4 paper, unconstrained student handwriting). The lecturer provides only a handwritten reference solution (100%) and a short set of grading rules; the reference is converted into a text-only summary that conditions grading without exposing the reference scan. Reliability is achieved through a multi-stage design with a format/presence check to prevent grading blank answers, an ensemble of independent graders, supervisor aggregation, and rigid templates with deterministic validation to produce auditable, machine-parseable reports. We evaluate the frozen pipeline in a clean-room protocol on a held-out real course quiz in Slovenian, including hand-drawn circuit schematics. With state-of-the-art backends (GPT-5.2 and Gemini-3 Pro), the full pipeline achieves $\approx$8-point mean absolute difference to lecturer grades with low bias and an estimated manual-review trigger rate of $\approx$17% at $D_{\max}=40$. Ablations show that trivial prompting and removing the reference solution substantially degrade accuracy and introduce systematic over-grading, confirming that structured prompting and reference grounding are essential.
Abstract (translated)
手写STEM考试能够捕捉到开放性推理和图表，但人工评分既缓慢又难以扩展。我们提出了一种端到端的工作流程，用于使用多模态大型语言模型（LLM）对扫描的手写工程小测验进行评分，该工作流保持了标准的考试过程（A4纸张、不受限制的学生手写）。讲师只需提供一份手写的参考答案（100%正确率）和一组简短的评分规则；参考答案被转换成仅包含文本的摘要，以条件化评分而不暴露参考扫描。可靠性通过多阶段设计实现，包括格式/存在检查以防止空白答案被评分、独立评分级联、监督聚合以及使用确定性验证的固定模板来生成可审计和机器解析报告。 我们在一个清洁房协议中对一项保留的真实课程测验进行了冻结管道的评估，该测验包含手绘电路图，并且是用斯洛文尼亚语进行的。在最先进的后台（GPT-5.2 和 Gemini-3 Pro）支持下，整个工作流程与讲师评分相比达到了约8分的平均绝对差异，偏差较低，并且估计的手动审查触发率为约17%（当最大分数D为40时）。消融研究表明，简单的提示和移除参考答案会显著降低准确性并引入系统性的过度打分，确认结构化提示和参考依据是必不可少的。
URL
https://arxiv.org/abs/2601.00730
PDF
-
BeHGAN: Bengali Handwritten Word Generation from Plain Text Using Generative Adversarial Networks
2025-12-25 14:38:12Md. Rakibul Islam, Md. Kamrozzaman Bhuiyan, Safwan Muntasir, Arifur Rahman Jawad, Most. Sharmin Sultana SamuAbstract
Handwritten Text Recognition (HTR) is a well-established research area. In contrast, Handwritten Text Generation (HTG) is an emerging field with significant potential. This task is challenging due to the variation in individual handwriting styles. A large and diverse dataset is required to generate realistic handwritten text. However, such datasets are difficult to collect and are not readily available. Bengali is the fifth most spoken language in the world. While several studies exist for languages such as English and Arabic, Bengali handwritten text generation has received little attention. To address this gap, we propose a method for generating Bengali handwritten words. We developed and used a self-collected dataset of Bengali handwriting samples. The dataset includes contributions from approximately five hundred individuals across different ages and genders. All images were pre-processed to ensure consistency and quality. Our approach demonstrates the ability to produce diverse handwritten outputs from input plain text. We believe this work contributes to the advancement of Bengali handwriting generation and can support further research in this area.
Abstract (translated)
手写文本识别（HTR）是一个研究领域，已经相当成熟。相比之下，手写文本生成（HTG）则是一个新兴的、具有巨大潜力的研究方向。然而，由于个体书写风格的变化莫测，这一任务面临诸多挑战。为了生成逼真的手写文本，需要一个大型且多样化的数据集，但这类数据集难以收集并无法轻易获取。 孟加拉语是世界上使用人数第五多的语言。尽管对于英语和阿拉伯语等语言已有若干研究，但对于孟加拉语的手写文本生成却鲜有关注。为填补这一空白，我们提出了一种用于生成孟加拉语手写单词的方法。为此，我们开发并利用了一个由大约500人提供样本的自收集数据集，参与者来自不同的年龄段和性别群体。所有图像都经过预处理以确保一致性和质量。 我们的方法展示了将输入的纯文本转换为多样化的手写输出的能力。我们认为这项工作有助于推进孟加拉语的手写生成技术，并能够支持该领域进一步的研究。
URL
https://arxiv.org/abs/2512.21694
PDF
