The paper introduces a Hybrid Deep Learning (HDL) architecture that combines Auto-Learned Features (ALF) and Human-Engineered Features (HEF) for handwriting verification. ALF is extracted using a Two Channel Convolutional Neural Network (TC-CNN) or a Two Channel Autoencoder (TC-AE), while HEF is obtained via Gradient Structural Concavity (GSC) or Scale Invariant Feature Transform (SIFT). Experiments on 150,000 pairs of the word "AND" from 1,500 writers show that the HDL model using AE-GSC achieves 99.7% accuracy on a seen writer dataset and 92.16% on a shuffled writer dataset, outperforming CEDAR-FOX, and AE-SIFT performs comparably on unseen writers.
arXiv:2606. 08858v1 Announce Type: cross Abstract: The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps.
ExpertHTR is a unified vision‑language framework for handwritten text recognition that tackles the challenge of small, heterogeneous datasets by organizing structural annotations into a common Page‑Region‑Line representation. It defines four related training tasks—complete transcription, physical‑line coverage, text localization, and localized recognition—without extra manual labels. The model combines a jointly trained dense backbone with a sparse Mixture‑of‑Experts architecture, using Sparsegen routing and regularization to adaptively activate experts, achieving state‑of‑the‑art results on the IAM benchmark and outperforming general‑purpose OCR systems on most datasets.
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.
arXiv:2609.37195v1 Announce Type: new Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents. Not only do they cut down ti...
The paper evaluates whether incorporating the hierarchical structure of Tironian notes can improve automatic recognition of this complex Latin shorthand system. Experiments compare flat classifiers (ResNet18, ConvNeXt, Swin, ViT) with hierarchy‑aware models (HD‑CNN and routing approaches) on handwritten and manuscript samples, with and without few‑shot adaptation. Results show that hierarchical models outperform flat ones when no adaptation is applied, but flat models surpass them after few‑shot adaptation, indicating that hierarchy can aid recognition under non‑adapted conditions.
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.
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.
arXiv:2607. 15509v1 Announce Type: cross Abstract: We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers for cross-lingual handwritten optical character recognition.
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.
arXiv:2606. 24984v1 Announce Type: new Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning.
arXiv:2607. 18997v1 Announce Type: cross Abstract: Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital 'middleware' organizing graphical and textual hierarchies, is underexplored.
The paper introduces a new online signature verification framework that combines the augmented path signature (APS) descriptor with a T-Mamba model. APS applies time and basepoint augmentations followed by sliding-window path signatures, capturing geometric structures and nonlinear inter-channel interactions. T-Mamba, a hybrid of two temporal convolutional network blocks and a time-scanning Mamba, learns both local temporal patterns and global long-range dependencies, achieving state‑of‑the‑art equal error rates on three public benchmark datasets.