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New MuSTEL dataset boosts multilingual OCR with 14.5M synthetic text lines

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Researchers have introduced MuSTEL, a large-scale synthetic dataset designed to improve optical character recognition (OCR) systems, particularly for multilingual text lines. The dataset comprises over 14.5 million annotated text-line images, incorporating diverse elements such as mixed writing directions, varied fonts and styles within a single line, and realistic document-like appearances including blur and distortion. MuSTEL aims to enhance the training and evaluation of OCR models across languages like Persian, Arabic, and English, addressing challenges posed by their unique typographic and rendering conditions. AI

IMPACT Enhances OCR capabilities for multilingual documents, potentially improving accessibility and data extraction in diverse linguistic contexts.

RANK_REASON The item is a research paper introducing a new dataset for OCR. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MuSTEL dataset boosts multilingual OCR with 14.5M synthetic text lines

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The item is a research paper introducing a new dataset for OCR. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Majid Adibian, Hossein Saberi, Hossein Zeinali ·

    MuSTEL: A Large-Scale Multilingual Synthetic Text-Line Image Dataset for OCR

    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 analyzes the page layout and extracts text-line images, which are then transcribed by …

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