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.
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new Abstract: Handwritten Text Recognition (HTR) systems have become an indispensable tool for the digitization of historical documents.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...
To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manually. For a script that can be read by only a small...
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...
RefLAM is a pipeline that converts manuscript page images and clean transcriptions into validated, line-level ground truth for Arabic handwritten text recognition. It combines a deep‑learning page‑segmentation model, a multimodal large language model for structured OCR, and a diacritic‑agnostic fuzzy alignment engine that assigns a confidence score to each line, with a provable correctness guarantee for perfect scores. Using RefLAM, the authors achieved a 75× speedup over manual annotation and released AraMS‑28k, a dataset of 14 historical Arabic manuscripts with detailed annotations.
The paper introduces a data‑centric framework that enables end‑to‑end OCR for low‑resource scripts using only an unannotated text corpus and digital fonts, eliminating the need for manually annotated training images. A synthetic data engine generates realistic word, line, and page images with accurate script shaping, which are then used to train a YOLOv8 word detector and a convolutional recurrent recognizer via connectionist temporal classification. Applied to the Kashmiri Perso‑Arabic Nastaliq script, the system achieves a 0.39% character error rate on held‑out words and provides publicly released models, code, and a synthetic dataset of 285,864 text‑line images.
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...
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...
The paper evaluates eight open‑source lightweight Vision‑Language Models (up to 7 B parameters) for converting document images into structured JSON, focusing on heritage collections with historical handwriting and non‑standard layouts. It compares zero‑shot, few‑shot, and fine‑tuned settings, measuring performance with CER, ANLS*, and mAP‑F1, and examines the effects of hyperparameter tuning, image preprocessing, and multi‑stage training. The study demonstrates that adapted VLMs can serve as a sustainable, private alternative to commercial OCR systems for institutional archives.
The paper reports that scene text recognition models, while achieving 89–97% accuracy on standard benchmarks, perform significantly worse on rare word–trigram combinations, with a 10–18 point drop in accuracy at the rare‑word/rare‑trigram corner across multiple languages and models. Scaling the vision backbone improves overall accuracy but does not alleviate this corner‑specific deficit. The authors identify the autoregressive decoder’s lexical prior as the root cause and show that architectural changes—specifically moving from autoregressive to CTC decoding—yield the largest improvement for these rare compositions.
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.
arXiv:2607. 08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents.
The paper evaluates open-source OCR, LLM, and VLM systems on a high‑risk public sector task: extracting structured data from student application documents. Results show that VLMs generally outperform OCR+LLM pipelines, yet only 4 of 35 configurations achieve F1 scores above 0.5, with most combinations scoring below 0.25. Model size and input quality, especially preserving OCR structure, are critical factors influencing performance.