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:2610.34148v1 Announce Type: new Abstract: Optical character recognition (OCR) systems depend strongly on the scale and diversity of their training data. One approach to document OCR first analy...
arXiv:2607. 20385v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spoken by more than 110 million people across multiple countries.
The paper introduces an all‑in‑one multilingual scene text recognizer called ScriptMoE, which uses a script‑aware mixture‑of‑experts architecture to handle 10 scripts and 229 languages. It is built on a new large‑scale synthetic dataset, TextMuSS‑10M, and evaluated on the TextMuSS‑Bench, achieving 82.06% accuracy—1.31% higher than the best baseline. When integrated into the PP‑OCRv5 pipeline, ScriptMoE raises the end‑to‑end multilingual F1 score from 65.71% to 80.89%, slightly surpassing the best vision‑language model while using far fewer parameters.
This systematic literature review examines 97 studies on optical character recognition (OCR) from 2015 to 2025, tracing the evolution of AI models, application domains, data types, and linguistic coverage. It identifies key OCR models, evaluates their performance, strengths, and limitations, and highlights unresolved challenges such as limited resources for underrepresented languages, high variability in handwritten text, and constraints in real‑time applications. The review proposes promising approaches—including self‑supervised learning, multimodal AI, AutoML, AI‑assisted postprocessing, TinyML, and joint corpora creation—to enhance OCR accuracy and address these challenges for industrial use.
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.
UniLipi is a unified multi‑script OCR model trained on 13 Indic scripts to recognize handwritten manuscripts under challenging conditions such as varied line geometry, length, and interruptions by non‑textual elements. It uses script‑aware synthetic data generation to perform well even with limited real annotated data. The model also predicts script identity and per‑line character counts, aiding manuscript cataloging, and its representations transfer to contemporary Indic handwriting and several non‑Indic scripts.
The paper introduces Synth-JDoc, a synthetic Japanese document image dataset created by rendering text with HTML and CSS to produce multi‑column layouts that include both vertical and horizontal writing styles. Images generated by text‑to‑image models are embedded to enhance visual realism, and noise and degradation filters are applied to improve robustness. Experiments show that fine‑tuning Large Vision Language Models on Synth‑JDoc yields superior performance on reading vertically written Japanese text compared to prior synthetic datasets.
arXiv:2605. 16409v3 Announce Type: replace-cross Abstract: Optical character recognition (OCR) and multilingual scene-text understanding remain challenging for multimodal large language models (MLLMs), particularly in real-world images containing small or degraded text, cluttered layouts, occlusion, handwriting, and complex typography.
HunyuanOCR-1.5 is a lightweight, end‑to‑end OCR‑specialized vision‑language model that unifies document parsing, text spotting, information extraction, text‑image translation, and multi‑image document understanding. It builds on the HunyuanOCR‑1.0 architecture, improving efficiency with DFlash‑based OCR decoding for faster inference (6.37× Transformer speedup, 2.14× under vLLM) and enhancing capability through an Agentic Data Flow system that autonomously constructs high‑quality training data for long‑tail OCR tasks. The model achieves top‑tier performance on OmniDocBench v1.6 and sets new milestones in ancient‑script OCR, chart/table parsing, multilingual parsing, and hallucination evaluation, while remaining lightweight for deployment.
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.
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.
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...