Skew Correction and Text Line Extraction of Arabic Historical Documents

Zoizou, Abdelhay; Zarghili, Arsalane; Chaker, Ilham

doi:10.1007/978-3-030-32959-4_13

Cited by 3 publications

(1 citation statement)

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“…Therefore, the need for an Arabic historical document processing system for ancient calligraphic styles creates a new fruitful research dimension for OCR. Verily, during the Int J Elec & Comp Eng ISSN: 2088-8708  past five years, few works have been conducted to process Arabic historical documents [1]- [5]. The optical recognition of these documents is very challenging because they are written in calligraphic style, which is usually more difficult to recognize than normal handwriting.…”

Section: Introductionmentioning

confidence: 99%

A study of feature extraction for Arabic calligraphy characters recognition

Zoizou,

Errebiai,

Zarghili

et al. 2024

IJECE

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Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.

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Section: Introductionmentioning

confidence: 99%