MuSTEL dataset advances multilingual text-line recognition for OCR
MuSTEL: A Large-Scale Multilingual Synthetic Text-Line Image Dataset for OCR
Computer Vision and Pattern Recognition
Summary
OCR systems need lots of varied training data to read text accurately. Recognizing text lines in different languages like Persian, Arabic, and English is hard because they use different fonts, styles, and directions. The authors created MuSTEL, a huge set of over 14.5 million fake but realistic text-line images that mix these languages and styles. This resource helps train OCR tools to better handle mixed-language documents with varied looks.
What this means in practice
- •For ocr engine developers: Train and test OCR models to recognize mixed-script text lines in Persian, Arabic, and English under diverse visual conditions.
- •For document digitization teams: Improve accuracy of text-line extraction and recognition for multilingual documents with varied fonts and layouts.
Authors
Majid Adibian, Hossein Saberi, Hossein Zeinali
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 a recognition model. Multilingual line-level recognition involving Persian, Arabic, and English presents additional challenges because different writing directions, fonts, and styles may coexist within a single line. This paper introduces MuSTEL, a large-scale synthetic multilingual OCR dataset containing more than 14.5 million annotated text-line images, organized into a training partition and predefined small and large test sets. Samples are generated from natural-language corpora, including multilingual content already present in the source texts. The generation framework incorporates capability-aware font selection, within-line font and style changes, mixed-direction text rendering, highlighting, and underlining. A validated repository of 2,009 font files from 711 families supports broad typographic coverage, while varied backgrounds, blur, geometric distortion, skew, stroke width, and image save quality provide diverse document-like appearances. Statistical analysis characterizes the dataset's language composition, transcription lengths, lexical and character-sequence diversity, image dimensions, typography, and rendering conditions. MuSTEL provides a resource for training and evaluating multilingual line-level OCR systems and investigating recognition robustness across languages, writing directions, and visual conditions.