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Related Experiment Video

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Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

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Multilingual character recognition dataset for Moroccan official documents.

Ali Benaissa^1,2, Abdelkhalak Bahri¹, Ahmad El Allaoui³

¹Data Science and Competitive Intelligence Team (DSCI), ENSAH, Abdelmalek Essaadi University (UAE), Tetouan, Morocco.

|January 8, 2024

Summary

A new dataset for multilingual character recognition was created, featuring Arabic, French, and Tamazight scripts. This resource aids in digitizing Moroccan documents and developing advanced recognition models.

Keywords:

Character recognition Documents digitization Moroccan characters images Moroccan documents OCR dataset Printed characters

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Area of Science:

Computer Science
Artificial Intelligence
Natural Language Processing

Background:

Multilingual character recognition is crucial for digitizing diverse documents.
Existing datasets may not adequately cover specific linguistic and script variations found in Moroccan official documents.
Advancements in character recognition necessitate large-scale, annotated datasets.

Purpose of the Study:

To construct a comprehensive dataset for multilingual character recognition tailored to Moroccan official documents.
To support the digitization of cultural heritage and archival materials.
To facilitate the development of robust automated text recognition systems.

Main Methods:

Programmatic dataset construction using Python scripts.
Inclusion of diverse character sets: Arabic, French, Tamazight (Tifinagh), digits, symbols, and special characters.
Generation of multiple character image variations from representative fonts for enhanced data diversity.

Main Results:

A dataset comprising sub-datasets for uppercase (26 classes), lowercase (26 classes), digits (9 classes), Arabic (28 classes), Tifinagh letters (33 classes), symbols (14 classes), and French special characters (16 classes).
Programmatically generated data ensuring variety and suitability for training recognition models.
A foundational resource for improving character recognition accuracy across multiple scripts.

Conclusions:

The developed dataset is essential for advancing multilingual character recognition, particularly for Moroccan contexts.
It enables more effective digitization of historical and official documents.
Contributes to the broader field of pattern recognition and artificial intelligence applications.