Handwriting
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Seeing Justice Clearly: Handwritten Legal Document Translation with OCR and Vision-Language Models
2025-12-19 19:06:14Shubham Kumar Nigam, Parjanya Aditya Shukla, Noel Shallum, Arnab BhattacharyaAbstract
Handwritten text recognition (HTR) and machine translation continue to pose significant challenges, particularly for low-resource languages like Marathi, which lack large digitized corpora and exhibit high variability in handwriting styles. The conventional approach to address this involves a two-stage pipeline: an OCR system extracts text from handwritten images, which is then translated into the target language using a machine translation model. In this work, we explore and compare the performance of traditional OCR-MT pipelines with Vision Large Language Models that aim to unify these stages and directly translate handwritten text images in a single, end-to-end step. Our motivation is grounded in the urgent need for scalable, accurate translation systems to digitize legal records such as FIRs, charge sheets, and witness statements in India's district and high courts. We evaluate both approaches on a curated dataset of handwritten Marathi legal documents, with the goal of enabling efficient legal document processing, even in low-resource environments. Our findings offer actionable insights toward building robust, edge-deployable solutions that enhance access to legal information for non-native speakers and legal professionals alike.
Abstract (translated)
手写文本识别（HTR）和机器翻译在像马拉地语这样资源匮乏的语言中仍面临重大挑战，这些语言缺乏大规模数字化语料库，并且手写风格具有高度的变异性。传统方法是采用两阶段管道：光学字符识别（OCR）系统从手写图像中提取文本，然后使用机器翻译模型将该文本翻译成目标语言。在这项工作中，我们探讨并比较了传统OCR-MT管道与旨在统一这些步骤并将手写文本图像直接转换为另一种语言的视觉大规模语言模型的表现。我们的动机是基于印度地方法院和高等法院数字化法律记录（如第一信息报告、起诉书和证人陈述）对可扩展且准确翻译系统的迫切需求。 我们在精心策划的手写马拉地语法律文件数据集上评估了这两种方法，旨在实现低资源环境下的高效法律文档处理。我们的研究结果为构建稳健的边缘部署解决方案提供了可行见解，这些方案能够提高非母语者和法律专业人士获取法律信息的途径。
URL
https://arxiv.org/abs/2512.18004
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Capacitive Touchscreens at Risk: Recovering Handwritten Trajectory on Smartphone via Electromagnetic Emanations
2025-12-12 11:33:05Yukun Cheng, Shiyu Zhu, Changhai Ou, Xingshuo Han, Yuan Li, Shihui ZhengAbstract
This paper reveals and exploits a critical security vulnerability: the electromagnetic (EM) side channel of capacitive touchscreens leaks sufficient information to recover fine-grained, continuous handwriting trajectories. We present Touchscreen Electromagnetic Side-channel Leakage Attack (TESLA), a non-contact attack framework that captures EM signals generated during on-screen writing and regresses them into two-dimensional (2D) handwriting trajectories in real time. Extensive evaluations across a variety of commercial off-the-shelf (COTS) smartphones show that TESLA achieves 77% character recognition accuracy and a Jaccard index of 0.74, demonstrating its capability to recover highly recognizable motion trajectories that closely resemble the original handwriting under realistic attack conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2512.11484
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HTR-ConvText: Leveraging Convolution and Textual Information for Handwritten Text Recognition
2025-12-04 17:35:05Pham Thach Thanh Truc, Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le DuyAbstract
Handwritten Text Recognition remains challenging due to the limited data, high writing style variance, and scripts with complex diacritics. Existing approaches, though partially address these issues, often struggle to generalize without massive synthetic data. To address these challenges, we propose HTR-ConvText, a model designed to capture fine-grained, stroke-level local features while preserving global contextual dependencies. In the feature extraction stage, we integrate a residual Convolutional Neural Network backbone with a MobileViT with Positional Encoding block. This enables the model to both capture structural patterns and learn subtle writing details. We then introduce the ConvText encoder, a hybrid architecture combining global context and local features within a hierarchical structure that reduces sequence length for improved efficiency. Additionally, an auxiliary module injects textual context to mitigate the weakness of Connectionist Temporal Classification. Evaluations on IAM, READ2016, LAM and HANDS-VNOnDB demonstrate that our approach achieves improved performance and better generalization compared to existing methods, especially in scenarios with limited training samples and high handwriting diversity.
Abstract (translated)
手写文本识别（Handwritten Text Recognition，HTR）仍然面临挑战，原因在于数据量有限、书写风格变化大以及复杂音调符号的使用。现有的方法尽管部分解决了这些问题，但在没有大量合成数据的情况下往往难以泛化。为应对这些挑战，我们提出了HTR-ConvText模型，该模型旨在捕捉细粒度的笔画级别的局部特征，同时保持全局上下文依赖性。在特征提取阶段，我们将残差卷积神经网络（Residual Convolutional Neural Network）骨干网与带有位置编码块的MobileViT集成在一起，使模型既能捕获结构模式又能学习微妙的书写细节。 接着我们引入了ConvText编码器，这是一种结合全局上下文和局部特征的混合架构，在层次化结构中减少了序列长度以提高效率。此外，一个辅助模块注入文本上下文以减轻连接时序分类（Connectionist Temporal Classification）方法的弱点。 在IAM、READ2016、LAM以及HANDS-VNOnDB数据集上的评估表明，我们的方法相比现有技术实现了更好的性能和泛化能力，尤其是在训练样本有限且手写风格多样化的场景下。
URL
https://arxiv.org/abs/2512.05021
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DNA: Dual-branch Network with Adaptation for Open-Set Online Handwriting Generation
2025-11-27 03:30:22Tsai-Ling Huang, Nhat-Tuong Do-Tran, Ngoc-Hoang-Lam Le, Hong-Han Shuai, Ching-Chun HuangAbstract
Online handwriting generation (OHG) enhances handwriting recognition models by synthesizing diverse, human-like samples. However, existing OHG methods struggle to generate unseen characters, particularly in glyph-based languages like Chinese, limiting their real-world applicability. In this paper, we introduce our method for OHG, where the writer's style and the characters generated during testing are unseen during training. To tackle this challenge, we propose a Dual-branch Network with Adaptation (DNA), which comprises an adaptive style branch and an adaptive content branch. The style branch learns stroke attributes such as writing direction, spacing, placement, and flow to generate realistic handwriting. Meanwhile, the content branch is designed to generalize effectively to unseen characters by decomposing character content into structural information and texture details, extracted via local and global encoders, respectively. Extensive experiments demonstrate that our DNA model is well-suited for the unseen OHG setting, achieving state-of-the-art performance.
Abstract (translated)
URL
https://arxiv.org/abs/2511.22064
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Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
2025-11-25 18:24:11Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares J. Abu-Dakka, Abdalla SwikirAbstract
Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S$^2$-NNDS, a learning-from-demonstration framework that simultaneously learns expressive neural dynamical systems alongside neural Lyapunov stability and barrier safety certificates. Unlike traditional approaches with restrictive polynomial parameterizations, S$^2$-NNDS leverages neural networks to capture complex robot motions providing probabilistic guarantees through split conformal prediction in learned certificates. Experimental results on various 2D and 3D datasets -- including LASA handwriting and demonstrations recorded kinesthetically from the Franka Emika Panda robot -- validate S$^2$-NNDS effectiveness in learning robust, safe, and stable motions from potentially unsafe demonstrations.
Abstract (translated)
URL
https://arxiv.org/abs/2511.20593
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ScriptViT: Vision Transformer-Based Personalized Handwriting Generation
2025-11-23 06:38:23Sajjan Acharya, Rajendra BaskotaAbstract
Styled handwriting generation aims to synthesize handwritten text that looks both realistic and aligned with a specific writer's style. While recent approaches involving GAN, transformer and diffusion-based models have made progress, they often struggle to capture the full spectrum of writer-specific attributes, particularly global stylistic patterns that span long-range spatial dependencies. As a result, capturing subtle writer-specific traits such as consistent slant, curvature or stroke pressure, while keeping the generated text accurate is still an open problem. In this work, we present a unified framework designed to address these limitations. We introduce a Vision Transformer-based style encoder that learns global stylistic patterns from multiple reference images, allowing the model to better represent long-range structural characteristics of handwriting. We then integrate these style cues with the target text using a cross-attention mechanism, enabling the system to produce handwritten images that more faithfully reflect the intended style. To make the process more interpretable, we utilize Salient Stroke Attention Analysis (SSAA), which reveals the stroke-level features the model focuses on during style transfer. Together, these components lead to handwriting synthesis that is not only more stylistically coherent, but also easier to understand and analyze.
Abstract (translated)
URL
https://arxiv.org/abs/2511.18307
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A Robust Task-Level Control Architecture for Learned Dynamical Systems
2025-11-12 22:45:32Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira HovakimyanAbstract
Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation (`task') space of robotic systems. However, the realization of the generated motion plans is often compromised by a ''task-execution mismatch'', where unmodeled dynamics, persistent disturbances, and system latency cause the robot's actual task-space state to diverge from the desired motion trajectory. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.
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URL
https://arxiv.org/abs/2511.09790
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Theoretical Analysis of Power-law Transformation on Images for Text Polarity Detection
2025-11-11 07:10:55Narendra Singh Yadav, Pavan Kumar PerepuAbstract
Several computer vision applications like vehicle license plate recognition, captcha recognition, printed or handwriting character recognition from images etc., text polarity detection and binarization are the important preprocessing tasks. To analyze any image, it has to be converted to a simple binary image. This binarization process requires the knowledge of polarity of text in the images. Text polarity is defined as the contrast of text with respect to background. That means, text is darker than the background (dark text on bright background) or vice-versa. The binarization process uses this polarity information to convert the original colour or gray scale image into a binary image. In the literature, there is an intuitive approach based on power-law transformation on the original images. In this approach, the authors have illustrated an interesting phenomenon from the histogram statistics of the transformed images. Considering text and background as two classes, they have observed that maximum between-class variance between two classes is increasing (decreasing) for dark (bright) text on bright (dark) background. The corresponding empirical results have been presented. In this paper, we present a theoretical analysis of the above phenomenon.
Abstract (translated)
URL
https://arxiv.org/abs/2511.07916
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Psychological stress during Examination and its estimation by handwriting in answer script
2025-11-08 09:03:50Abhijeet Kumar, Chetan Agarwal, Pronoy B. Neogi, Mayank GoswamiAbstract
This research explores the fusion of graphology and artificial intelligence to quantify psychological stress levels in students by analyzing their handwritten examination scripts. By leveraging Optical Character Recognition and transformer based sentiment analysis models, we present a data driven approach that transcends traditional grading systems, offering deeper insights into cognitive and emotional states during examinations. The system integrates high resolution image processing, TrOCR, and sentiment entropy fusion using RoBERTa based models to generate a numerical Stress Index. Our method achieves robustness through a five model voting mechanism and unsupervised anomaly detection, making it an innovative framework in academic forensics.
Abstract (translated)
URL
https://arxiv.org/abs/2511.11633
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Understanding Cross Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation
2025-11-08 04:13:01Changqing Gong, Huafeng Qin, Mounim A. El-YacoubiAbstract
Alzheimer's disease is a prevalent neurodegenerative disorder for which early detection is critical. Handwriting-often disrupted in prodromal AD-provides a non-invasive and cost-effective window into subtle motor and cognitive decline. Existing handwriting-based AD studies, mostly relying on online trajectories and hand-crafted features, have not systematically examined how task type influences diagnostic performance and cross-task generalization. Meanwhile, large-scale vision language models have demonstrated remarkable zero or few-shot anomaly detection in natural images and strong adaptability across medical modalities such as chest X-ray and brain MRI. However, handwriting-based disease detection remains largely unexplored within this paradigm. To close this gap, we introduce a lightweight Cross-Layer Fusion Adapter framework that repurposes CLIP for handwriting-based AD screening. CLFA implants multi-level fusion adapters within the visual encoder to progressively align representations toward handwriting-specific medical cues, enabling prompt-free and efficient zero-shot inference. Using this framework, we systematically investigate cross-task generalization-training on a specific handwriting task and evaluating on unseen ones-to reveal which task types and writing patterns most effectively discriminate AD. Extensive analyses further highlight characteristic stroke patterns and task-level factors that contribute to early AD identification, offering both diagnostic insights and a benchmark for handwriting-based cognitive assessment.
Abstract (translated)
URL
https://arxiv.org/abs/2511.05841
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XFlowMP: Task-Conditioned Motion Fields for Generative Robot Planning with Schrodinger Bridges
Abstract
Generative robotic motion planning requires not only the synthesis of smooth and collision-free trajectories but also feasibility across diverse tasks and dynamic constraints. Prior planning methods, both traditional and generative, often struggle to incorporate high-level semantics with low-level constraints, especially the nexus between task configurations and motion controllability. In this work, we present XFlowMP, a task-conditioned generative motion planner that models robot trajectory evolution as entropic flows bridging stochastic noises and expert demonstrations via Schrodinger bridges given the inquiry task configuration. Specifically, our method leverages Schrodinger bridges as a conditional flow matching coupled with a score function to learn motion fields with high-order dynamics while encoding start-goal configurations, enabling the generation of collision-free and dynamically-feasible motions. Through evaluations, XFlowMP achieves up to 53.79% lower maximum mean discrepancy, 36.36% smoother motions, and 39.88% lower energy consumption while comparing to the next-best baseline on the RobotPointMass benchmark, and also reducing short-horizon planning time by 11.72%. On long-horizon motions in the LASA Handwriting dataset, our method maintains the trajectories with 1.26% lower maximum mean discrepancy, 3.96% smoother, and 31.97% lower energy. We further demonstrate the practicality of our method on the Kinova Gen3 manipulator, executing planning motions and confirming its robustness in real-world settings.
Abstract (translated)
生成式机器人运动规划不仅要求合成平滑且无碰撞的轨迹，还需确保在多样任务和动态约束下的可行性。先前的传统方法和生成方法往往难以将高层次语义与低层次约束结合起来，特别是在任务配置和运动可控性之间的连接上尤为困难。为此，我们提出了XFlowMP，这是一种基于任务条件的生成式运动规划器，它通过薛定谔桥（Schrodinger bridges）来建模机器人的轨迹演化，将随机噪声和专家演示联系起来，在给定查询任务配置的情况下进行工作。具体来说，我们的方法利用了薛定谔桥作为条件流匹配并结合评分函数来学习具有高阶动力学的运动场，并编码起点-终点配置，从而能够生成无碰撞且动态可行性的动作。 通过评估，XFlowMP在RobotPointMass基准测试中相较于最佳基线方案分别降低了最大均值差异（maximum mean discrepancy）达53.79%，提升了动作平滑度36.36%，减少了能耗39.88%。此外，在LASA Handwriting数据集的长时序运动上，我们的方法维持了轨迹的最大均值差异降低1.26%，动作更光滑（提升幅度为3.96%），且能量消耗降低了31.97%。我们进一步在Kinova Gen3机械臂上展示了该方法的实际应用性，在执行规划动作的同时验证其在真实环境中的鲁棒性能。 这项研究展示了一种创新性的生成式运动规划策略，不仅提升了机器人操作的灵活性和效率，也为未来机器人技术的发展奠定了基础。
URL
https://arxiv.org/abs/2512.00022
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Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification
2025-10-21 14:25:16Hanif Rasyidi, Moshiur FaraziAbstract
This paper investigates various factors that influence the performance of end-to-end deep learning approaches for historical writer identification (HWI), a task that remains challenging due to the diversity of handwriting styles, document degradation, and the limited number of labelled samples per writer. These conditions often make accurate recognition difficult, even for human experts. Traditional HWI methods typically rely on handcrafted image processing and clustering techniques, which tend to perform well on small and carefully curated datasets. In contrast, end-to-end pipelines aim to automate the process by learning features directly from document images. However, our experiments show that many of these models struggle to generalise in more realistic, document-level settings, especially under zero-shot scenarios where writers in the test set are not present in the training data. We explore different combinations of pre-processing methods, backbone architectures, and post-processing strategies, including text segmentation, patch sampling, and feature aggregation. The results suggest that most configurations perform poorly due to weak capture of low-level visual features, inconsistent patch representations, and high sensitivity to content noise. Still, we identify one end-to-end setup that achieves results comparable to the top-performing system, despite using a simpler design. These findings point to key challenges in building robust end-to-end systems and offer insight into design choices that improve performance in historical document writer identification.
Abstract (translated)
本文研究了影响端到端深度学习方法在历史书写者识别（HWI）任务中的各种因素。由于手写风格的多样性、文档退化以及每位书写者的标注样本数量有限，这一任务仍然具有挑战性，甚至人类专家也难以准确地进行识别。传统的历史书写者识别方法通常依赖于手工制作的图像处理和聚类技术，在经过精心策划的小型数据集上表现出色。相比之下，端到端流程旨在通过直接从文档图像中学习特征来自动化整个过程。然而，我们的实验表明，许多这些模型在更加现实、以文档为单位的情境下难以泛化，特别是在零样本场景（测试集中出现的书写者不在训练数据中）中表现尤其不佳。 本文探讨了不同的预处理方法、主干架构和后处理策略组合的效果，包括文本分割、补丁采样以及特征聚合。结果显示，大多数配置由于对低级视觉特征捕捉能力弱、补丁表示不一致以及内容噪声敏感度高而表现较差。尽管如此，我们发现有一种端到端设置在使用更为简单的设计时仍能取得与最佳系统相媲美的结果。 这些发现揭示了构建稳健的端到端系统的挑战，并为改善历史文档书写者识别中的性能提供了关于设计选择的见解。
URL
https://arxiv.org/abs/2510.18671
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Signature Forgery Detection: Improving Cross-Dataset Generalization
Abstract
Automated signature verification is a critical biometric technique used in banking, identity authentication, and legal documentation. Despite the notable progress achieved by deep learning methods, most approaches in offline signature verification still struggle to generalize across datasets, as variations in handwriting styles and acquisition protocols often degrade performance. This study investigates feature learning strategies for signature forgery detection, focusing on improving cross-dataset generalization -- that is, model robustness when trained on one dataset and tested on another. Using three public benchmarks -- CEDAR, ICDAR, and GPDS Synthetic -- two experimental pipelines were developed: one based on raw signature images and another employing a preprocessing method referred to as shell preprocessing. Several behavioral patterns were identified and analyzed; however, no definitive superiority between the two approaches was established. The results show that the raw-image model achieved higher performance across benchmarks, while the shell-based model demonstrated promising potential for future refinement toward robust, cross-domain signature verification.
Abstract (translated)
自动签名验证是一种在银行、身份认证和法律文件中至关重要的生物识别技术。尽管深度学习方法已取得了显著的进步，但在离线签名验证领域，大多数方法仍然难以跨数据集泛化，因为书写风格的差异以及获取协议的不同往往会导致性能下降。本研究调查了用于签名伪造检测的功能学习策略，并专注于改进跨数据集的泛化能力——即，在一个数据集上训练模型并在另一个数据集上进行测试时的模型鲁棒性。 该研究使用三个公共基准——CEDAR、ICDAR和GPDS合成，开发了两个实验流程：一种基于原始签名图像，另一种采用被称为“壳预处理”的预处理方法。在识别并分析了几种行为模式后，并未明确证明这两种方法中哪一种更优越。结果显示，基于原始图像的模型在所有基准测试中的表现更好，而基于“壳”的模型展示了在未来进行优化以实现稳健、跨领域的签名验证的巨大潜力。
URL
https://arxiv.org/abs/2510.17724
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ClapperText: A Benchmark for Text Recognition in Low-Resource Archival Documents
2025-10-17 11:44:08Tingyu Lin, Marco Peer, Florian Kleber, Robert SablatnigAbstract
This paper presents ClapperText, a benchmark dataset for handwritten and printed text recognition in visually degraded and low-resource settings. The dataset is derived from 127 World War II-era archival video segments containing clapperboards that record structured production metadata such as date, location, and camera-operator identity. ClapperText includes 9,813 annotated frames and 94,573 word-level text instances, 67% of which are handwritten and 1,566 are partially occluded. Each instance includes transcription, semantic category, text type, and occlusion status, with annotations available as rotated bounding boxes represented as 4-point polygons to support spatially precise OCR applications. Recognizing clapperboard text poses significant challenges, including motion blur, handwriting variation, exposure fluctuations, and cluttered backgrounds, mirroring broader challenges in historical document analysis where structured content appears in degraded, non-standard forms. We provide both full-frame annotations and cropped word images to support downstream tasks. Using a consistent per-video evaluation protocol, we benchmark six representative recognition and seven detection models under zero-shot and fine-tuned conditions. Despite the small training set (18 videos), fine-tuning leads to substantial performance gains, highlighting ClapperText's suitability for few-shot learning scenarios. The dataset offers a realistic and culturally grounded resource for advancing robust OCR and document understanding in low-resource archival contexts. The dataset and evaluation code are available at this https URL.
Abstract (translated)
这篇论文介绍了ClapperText，这是一个用于在视觉退化和低资源环境下识别手写和印刷文本的基准数据集。该数据集来源于包含 clapboard（场记板）的127段第二次世界大战时期的档案视频片段，这些 clapboard 记录了生产元数据，如日期、地点和摄像师身份等结构化信息。ClapperText 包含9,813张注释图像帧和94,573个单词级别的文本实例，其中67%为手写文本，并且有1,566个部分被遮挡的实例。每个实例包括转录、语义类别、文本类型以及遮挡状态的信息，注释以旋转边界框的形式提供（表示为四点多边形），以便支持空间精确的OCR应用。 识别 clapboard 上的文字面临着许多挑战，比如运动模糊、手写风格的变化、曝光度波动和杂乱背景等。这些问题反映了在历史文档分析中遇到的更广泛的问题，在这种情况下，结构化内容以退化的非标准形式出现。我们提供全帧注释及裁剪后的单词图像，支持下游任务需求。 通过一致的视频级评估协议，我们在零样本（zero-shot）和微调条件下对六个代表性识别模型和七个检测模型进行了基准测试。尽管训练集规模较小（18段视频），但微调能够带来显著性能提升，这突显了ClapperText在少量数据学习场景中的适用性。 该数据集为推进低资源档案环境下的稳健OCR技术和文档理解提供了现实且文化相关的资源。数据集和评估代码可在[此处](https://this https URL)获取。
URL
https://arxiv.org/abs/2510.15557
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Enhancing OCR for Sino-Vietnamese Language Processing via Fine-tuned PaddleOCRv5
2025-10-05 02:34:38Minh Hoang Nguyen, Su Nguyen ThietAbstract
Recognizing and processing Classical Chinese (Han-Nom) texts play a vital role in digitizing Vietnamese historical documents and enabling cross-lingual semantic research. However, existing OCR systems struggle with degraded scans, non-standard glyphs, and handwriting variations common in ancient sources. In this work, we propose a fine-tuning approach for PaddleOCRv5 to improve character recognition on Han-Nom texts. We retrain the text recognition module using a curated subset of ancient Vietnamese Chinese manuscripts, supported by a full training pipeline covering preprocessing, LMDB conversion, evaluation, and visualization. Experimental results show a significant improvement over the base model, with exact accuracy increasing from 37.5 percent to 50.0 percent, particularly under noisy image conditions. Furthermore, we develop an interactive demo that visually compares pre- and post-fine-tuning recognition results, facilitating downstream applications such as Han-Vietnamese semantic alignment, machine translation, and historical linguistics research. The demo is available at this https URL.
Abstract (translated)
识别和处理古典中文（汉喃文）文本在越南历史文献的数字化及跨语言语义研究中扮演着至关重要的角色。然而，现有的OCR系统在处理古代资料中存在的退化扫描、非标准字符和手写变体时面临挑战。为此，我们提出了一种针对PaddleOCRv5的微调方法，以提高对汉喃文字符识别的准确率。我们将文本识别模块重新训练于精心挑选的一组越南古中文手稿子集上，并且提供了一个完整的培训流程，涵盖了预处理、LMDB转换、评估和可视化。实验结果显示，在噪声图像条件下，与基础模型相比，我们的方法在精确度方面有显著提升，从37.5%提高到了50.0%。 此外，我们还开发了一款交互式演示程序，该程序能直观地比较微调前后的识别结果，有助于下游应用如汉越语义对齐、机器翻译及历史语言学研究。这款演示工具可在以下链接中访问：[https URL]（请将方括号内的文本替换为实际的URL）。
URL
https://arxiv.org/abs/2510.04003
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DiffInk: Glyph- and Style-Aware Latent Diffusion Transformer for Text to Online Handwriting Generation
2025-09-28 03:58:15Wei Pan, Huiguo He, Hiuyi Cheng, Yilin Shi, Lianwen JinAbstract
Deep generative models have advanced text-to-online handwriting generation (TOHG), which aims to synthesize realistic pen trajectories conditioned on textual input and style references. However, most existing methods still primarily focus on character- or word-level generation, resulting in inefficiency and a lack of holistic structural modeling when applied to full text lines. To address these issues, we propose DiffInk, the first latent diffusion Transformer framework for full-line handwriting generation. We first introduce InkVAE, a novel sequential variational autoencoder enhanced with two complementary latent-space regularization losses: (1) an OCR-based loss enforcing glyph-level accuracy, and (2) a style-classification loss preserving writing style. This dual regularization yields a semantically structured latent space where character content and writer styles are effectively disentangled. We then introduce InkDiT, a novel latent diffusion Transformer that integrates target text and reference styles to generate coherent pen trajectories. Experimental results demonstrate that DiffInk outperforms existing state-of-the-art methods in both glyph accuracy and style fidelity, while significantly improving generation efficiency. Code will be made publicly available.
Abstract (translated)
深度生成模型在文本到在线手写生成（TOHG）方面取得了进展，该技术旨在根据文本输入和风格参考合成逼真的笔迹轨迹。然而，大多数现有的方法仍然主要集中在字符级或单词级别的生成上，在应用于整个文本行时会导致效率低下且缺乏整体结构建模。为了应对这些挑战，我们提出了DiffInk，这是首个用于完整文本行手写生成的潜在扩散Transformer框架。 我们首先引入了InkVAE，这是一种新颖的序列变分自编码器，增强了两种互补的潜在空间正则化损失：(1) 基于OCR的损失，强制要求字符级别的准确性；(2) 保持书写风格的风格分类损失。这种双重正则化产生了语义结构化的潜在空间，在其中可以有效地分离出字符内容和写作风格。 然后我们引入了InkDiT，这是一种新颖的潜在扩散Transformer模型，它可以整合目标文本和参考风格来生成连贯的笔迹轨迹。 实验结果表明，DiffInk在字符准确性、风格保真度方面均优于现有的最先进的方法，并且显著提高了生成效率。代码将在公开发布。
URL
https://arxiv.org/abs/2509.23624
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TrueGradeAI: Retrieval-Augmented and Bias-Resistant AI for Transparent and Explainable Digital Assessments
2025-09-26 16:00:36Rakesh Thakur, Shivaansh Kaushik, Gauri Chopra, Harsh RohillaAbstract
This paper introduces TrueGradeAI, an AI-driven digital examination framework designed to overcome the shortcomings of traditional paper-based assessments, including excessive paper usage, logistical complexity, grading delays, and evaluator bias. The system preserves natural handwriting by capturing stylus input on secure tablets and applying transformer-based optical character recognition for transcription. Evaluation is conducted through a retrieval-augmented pipeline that integrates faculty solutions, cache layers, and external references, enabling a large language model to assign scores with explicit, evidence-linked reasoning. Unlike prior tablet-based exam systems that primarily digitize responses, TrueGradeAI advances the field by incorporating explainable automation, bias mitigation, and auditable grading trails. By uniting handwriting preservation with scalable and transparent evaluation, the framework reduces environmental costs, accelerates feedback cycles, and progressively builds a reusable knowledge base, while actively working to mitigate grading bias and ensure fairness in assessment.
Abstract (translated)
本文介绍了TrueGradeAI，这是一个基于人工智能的数字考试框架，旨在克服传统纸质评估方法的不足之处，包括过度使用纸张、物流复杂性、评分延迟和评卷人偏见等问题。该系统通过在安全平板电脑上捕捉触控笔输入来保留自然书写，并采用基于转换器的光学字符识别技术进行转录。评估过程则通过一个增强检索功能的工作流程完成，这一工作流程整合了教师提供的答案方案、缓存层以及外部参考文献，使大型语言模型能够依据具体证据给出评分并提供明确的理由说明。 与以往仅仅数字化考生回答的平板电脑考试系统不同，TrueGradeAI在解释性自动化、偏见缓解和可追溯评分记录方面取得了进步。通过结合书写保存和规模化的透明评估方式，该框架降低了环境成本，加速了反馈周期，并逐步建立了一个可重复使用的知识库，同时积极努力减少评分偏见并确保评价的公平性。
URL
https://arxiv.org/abs/2509.22516
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GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
2025-09-22 17:56:17Md. Mahmudul Hasan, Ahmed Nesar Tahsin Choudhury, Mahmudul Hasan, Md. Mosaddek KhanAbstract
Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script--featuring conjuncts, diacritics, and highly variable handwriting styles--combined with a scarcity of annotated datasets makes this task particularly challenging. We present GraDeT-HTR, a resource-efficient Bengali handwritten text recognition system based on a Grapheme-aware Decoder-only Transformer architecture. To address the unique challenges of Bengali script, we augment the performance of a decoder-only transformer by integrating a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.
Abstract (translated)
尽管孟加拉语是世界上使用人数第六多的语言，但孟加拉语的手写文本识别（HTR）系统仍然严重缺乏发展。孟加拉语的复杂性——包括连字符、声调符号以及高度变化的手写风格——再加上标注数据集的匮乏，使得这一任务尤其具有挑战性。我们提出了GraDeT-HTR，这是一个基于图素感知解码器独奏变压器架构的资源高效型孟加拉语文本识别系统。为了应对孟加拉语特有的挑战，我们在一个解码器独奏的变压器中整合了基于图素的标记器，并证明与传统的子词标记器相比，这能显著提高识别准确性。我们的模型在大规模合成数据上进行了预训练，并通过真实的标注样本进行微调，在多个基准数据集上实现了最佳性能。
URL
https://arxiv.org/abs/2509.18081
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Cross-Corpus and Cross-domain Handwriting Assessment of NeuroDegenerative Diseases via Time-Series-to-Image Conversion
2025-09-20 00:00:55Gabrielle Chavez, Laureano Moro-Velazquez, Ankur Butala, Najim Dehak, Thomas ThebaudAbstract
Handwriting is significantly affected by neurological disorders (ND) such as Parkinson's disease (PD) and Alzheimer's disease (AD). Prior works have analyzed handwriting tasks using feature-based approaches or computer-vision techniques, but these methods have struggled to generalize across multiple datasets, particularly between temporal features represented as time-series and images. We propose a framework that leverages both time-series and images of handwriting through a joint classifier, based on a ResNet50 pretrained on ImageNet-1k. Binary classification experiments demonstrate state-of-the-art performances on existing time-series and image datasets, with significant improvement on specific drawing and writing tasks from the NeuroLogical Signals (NLS) dataset. In particular, the proposed model demonstrates improved performance on Draw Clock and Spiral tasks. Additionally, cross-dataset and multi-dataset experiments were consistently able to achieve high F1 scores, up to 98 for PD detection, highlighting the potential of the proposed model to generalize over different forms of handwriting signals, and enhance the detection of motor deficits in ND.
Abstract (translated)
手写受到神经性疾病（ND）如帕金森病（PD）和阿尔茨海默病（AD）的显著影响。先前的研究使用基于特征的方法或计算机视觉技术来分析手写任务，但这些方法在多个数据集之间推广时遇到了困难，尤其是在时间序列表示的时间特征与图像之间的推广方面存在挑战。我们提出了一种框架，该框架通过一个基于ImageNet-1k上预训练的ResNet50的联合分类器同时利用了手写的时序数据和图像信息。 二元分类实验表明，在现有的时间序列和图像数据集上取得了最先进的性能，并且在来自NeuroLogical Signals（NLS）数据集的具体绘制和书写任务中有了显著改进，特别是提出的模型在绘制钟表和螺旋线任务中的表现有所提升。此外，跨数据集和多数据集的实验始终能够达到较高的F1分数，在PD检测方面最高可达98分，这突显了所提出模型推广不同形式的手写信号以及增强ND运动功能障碍检测的能力。
URL
https://arxiv.org/abs/2509.16474
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Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model
2025-09-19 06:53:17Sidra Hanif, Longin Jan LateckiAbstract
Handwriting stroke generation is crucial for improving the performance of tasks such as handwriting recognition and writers order recovery. In handwriting stroke generation, it is significantly important to imitate the sample calligraphic style. The previous studies have suggested utilizing the calligraphic features of the handwriting. However, they had not considered word spacing (word layout) as an explicit handwriting feature, which results in inconsistent word spacing for style imitation. Firstly, this work proposes multi-scale attention features for calligraphic style imitation. These multi-scale feature embeddings highlight the local and global style features. Secondly, we propose to include the words layout, which facilitates word spacing for handwriting stroke generation. Moreover, we propose a conditional diffusion model to predict strokes in contrast to previous work, which directly generated style images. Stroke generation provides additional temporal coordinate information, which is lacking in image generation. Hence, our proposed conditional diffusion model for stroke generation is guided by calligraphic style and word layout for better handwriting imitation and stroke generation in a calligraphic style. Our experimentation shows that the proposed diffusion model outperforms the current state-of-the-art stroke generation and is competitive with recent image generation networks.
Abstract (translated)
手写笔画生成对于提高诸如手写识别和书写顺序恢复等任务的性能至关重要。在手写笔画生成过程中，模仿样本书法风格显得尤为重要。之前的研究建议利用手写的书法特征，但是他们没有将单词间距（词布局）视为显式的书法特征，这导致了风格模仿中的不一致的单词间距问题。 首先，这项工作提出了多尺度注意力特性用于模仿书法风格。这些多尺度特性嵌入突出了局部和全局的风格特征。其次，我们提议引入词布局，以帮助手写笔画生成时保持适当的单词间距。此外，我们提出了一种条件扩散模型来预测笔画，这与之前直接生成风格图像的工作不同。笔画生成提供了缺失于图像生成中的额外时间坐标信息。因此，我们的提出的用于笔画生成的条件扩散模型通过书法样式和词布局来进行指导，以更好地模仿手写并以书法风格生成笔画。 实验表明，所提出的扩散模型在笔画生成方面优于当前最先进的方法，并且与最近的图像生成网络竞争激烈。
URL
https://arxiv.org/abs/2509.15678
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