The paper presents a two‑stage method for recovering handwriting trajectories from static images. First, it predicts ordered stroke instances autoregressively, then reconstructs continuous motion within each stroke using direction‑related cues. Experiments on Chinese, English, and Tamil handwriting show that this ordered prediction outperforms post‑hoc ordering and baseline models, and that sampling density significantly impacts performance.
By En-Guang Wang, Yan-Ming Zhang, Fei Yin, Cheng-Lin Liu
The paper introduces GraphemeNet, a unified multi‑script handwritten character recognition architecture that explicitly encodes script‑geometric regularities. It uses two orthogonal binary axes: Persistent Scaffold Injection (PSI) to embed stroke‑level geometry into each encoder stage, and a choice between gated global pooling or a Stroke Topology Module for spatial relational reasoning. Across fourteen benchmarks in eight writing systems, GraphemeNet achieves state‑of‑the‑art performance with fewer parameters, demonstrating the effectiveness of structural‑prior efficiency for multi‑script HCR.
By Ranjit Raut, Aarav Subedi, Ashim Shrestha
arXiv:2604.02103v3 Announce Type: replace-cross
Abstract: Realistic online handwriting depends not only on individual character shapes, but also on how a writer connects, spaces, and aligns adjacent...
By Jinsu Shin, Sungeun Hong, JinYeong Bak
arXiv:2607. 12500v1 Announce Type: new Abstract: Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications.
By Yataro Tamura, Brian Kenji Iwana, Jiseok Lee
arXiv:2607. 26733v1 Announce Type: cross Abstract: Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction.
By Wassim Swaileh, Florent Imbert, Yann Soullard, Romain Tavenard, Eric Anquetil
The paper presents a rotation‑free online handwritten character recognition system that uses Sliding Window Path Signature (SW‑PS) to extract local structural features and a lightweight Linear Recurrent Unit (LRU) classifier. The LRU blends the incremental processing of RNNs with the parallel training efficiency of state‑space models to model dynamic stroke characteristics. Experiments on rotated CASIA‑OLHWDB1.1 subsets (digits, English upper letters, Chinese radicals) achieved accuracies of 99.62%, 96.67%, and 94.33% respectively, outperforming competing models in convergence speed and test accuracy.
By Zhe Ling, Sicheng Yu, Danyu Yang
OmniHandwritingOCR is a diagnostic benchmark designed to evaluate multimodal large language models (MLLMs) and OCR systems on handwritten text and mathematical expression recognition. It comprises 77.57K labeled images across six subtasks and twelve subsets, including a difficulty‑stratified multi‑line formula corpus that tests robustness to increasing structural complexity. The benchmark reveals that current systems perform poorly on complex multi‑line formulas, exhibit variable rankings across languages and formula settings, and sometimes hallucinate corrections that are not visually supported.
By Zinuo Guo, Min Zhang, Bo Jiang
The paper introduces NormPaST‑Risk, a novel framework that detects Alzheimer’s disease from online handwriting by focusing on local, segment‑level risk rather than whole‑trajectory features. It employs a multi‑scale temporal encoder, a Paper‑Air state‑space model to separate on‑paper motor execution from in‑air planning, and a healthy‑normative branch to learn normal handwriting dynamics. A weakly supervised segment‑risk module identifies high‑risk handwriting segments, achieving superior AD/HC classification on the DARWIN benchmark and offering interpretable evidence linked to disease‑related handwriting changes.
By Changqing Gong, Huafeng Qin, Moun\^im A. El-Yacoubi
arXiv:2607. 26708v1 Announce Type: new Abstract: The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction.
By Florent Imbert, Eric Anquetil, Yann Soullard, Romain Tavenard
arXiv:2603. 16883v2 Announce Type: replace-cross Abstract: 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.
By Jindong Li, Dario Zanca, Vincent Christlein, Tim Hamann, Jens Barth, Peter K\"ampf, Bj\"orn Eskofier
arXiv:2607. 26736v1 Announce Type: new Abstract: Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation.
By Florent Imbert, Romain Tavenard, Yann Soullard, Eric Anquetil
arXiv:2609.10084v1 Announce Type: cross
Abstract: Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were n...
By Clarence Chew, Gim Siang Chia, Sukalpa Chanda, Subhroshekhar Ghosh, Soumendu Sundar Mukherjee