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