The paper presents a low‑resource pipeline for converting handwritten civil‑status registers in Sub‑Saharan Africa into searchable text using only a smartphone for capture and a laptop CPU for inference. It introduces a new corpus of 1,568 smartphone images from a Chadian municipal registry, and shows that a classical preprocessing stage plus a fine‑tuned Kraken recognizer can achieve a character error rate of 0.50% and a word error rate of 3.37% on a test set with writers seen during training. The recognizer is lightweight (61 MiB) and runs in under 6 s per image on a six‑core laptop, demonstrating that such a frugal system is feasible for local administrations lacking GPUs or reliable connectivity.
By Ousmane Hamadou, Jean Louis Kedieng Ebongue Fendji
The paper investigates self‑supervised pretraining for Handwritten Text Recognition (HTR) and finds that pixel‑reconstruction methods outperform contrastive methods, a contrast to natural‑image classification. The authors argue that discriminative information for HTR is concentrated in high‑variance pixel directions, and they test six SSL methods across six handwriting benchmarks in five languages, showing that pixel‑grounded approaches achieve the lowest character error rates and are more label‑efficient. When combined with a pretrained LLM decoder, a frozen pixel‑grounded encoder rivals fully fine‑tuned supervised baselines, underscoring the importance of aligning representations with high‑variance pixel subspaces.
By Carlos Garrido-Munoz, Jorge Calvo-Zaragoza
arXiv:2610.01134v1 Announce Type: new
Abstract: An optical character recognition (OCR) can scan a paper and extract text using technology, making people's jobs easier. While various OCR systems are a...
By Faias Satter, Sk. Md. Masudul Ahsan
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
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.
By John Pavlopoulos, Spyros Barbakos, Lavinia Ferretti, Dionysis Voulgarakis, Asimina Paparrigopoulou, Maria Konstantinidou, Giuseppe De Gregorio, Isabelle Marthot-Santaniello, Paraskevi Platanou, Holger Essler
arXiv:2608.22366v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remai...
By Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani
Sinhala is a morphologically rich abugida spoken by roughly 16 million people in Sri Lanka, and to date, there are no publicly available real-world datasets for page-level Sinhala OCR. All previous studies for assessing Sinhala OCR models have used artificially generated data.
The paper presents a multi‑stage framework for recognizing Kuzushiji characters in Japanese historical documents. It combines character detection, cropping, classification, reading‑order reconstruction via adaptive column clustering, and large‑language‑model‑based post‑OCR correction. The authors also augment data synthetically, correct dataset annotations, and introduce new test sets, achieving significant character error rate reductions on real, synthetic, and out‑of‑domain data.
By Rui-Yang Ju, Kohei Yamashita, Hirotaka Kameko, Shinsuke Mori
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.
By Seyed Mohammad Abuzar Hashemi, Mihir Chauhan, Jun Chu, Sargur Srihari
arXiv:2611.07810v1 Announce Type: new
Abstract: Handwritten signature verification (HSV) is a critical biometric technique in legal and forensic authentication, whose efficacy hinges on the discrimin...
By Peirong Zhang, Zhenhua Yang, Jiaxin Zhang, Moises Diaz, Miguel A. Ferrer, Umapada Pal, Wei Zhou, Lianwen Jin
The paper explores how synthetic data can be used to train a Thai OCR model without real OCR labels. By systematically varying factors such as typeface diversity, page structure, and handwriting glyphs, the authors identify which aspects of realism most improve transfer to real documents. Using these insights, they adapt a large PaddleOCR model into Wayu‑Paxa‑OCR‑Zero, achieving a median character error rate of 1.24% on printed pages and 20.55% on handwriting, outperforming existing Thai OCR systems.
By Kunat Pipatanakul
arXiv:2609.16859v1 Announce Type: cross
Abstract: To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manua...
By Manglesh Kumar Pandey, Sumit Kumar Banshal