Printed Arabic Characters Recognition Using Combined Features and CNN classifier

Bouchakour, Lallouani; Meziani, Fariza; Latrache, Houda; Ghribi, Khadija; Yahiaoui, Mustapha

doi:10.1109/icrami52622.2021.9585941

Cited by 3 publications

(1 citation statement)

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“…Bouchakour et al [89] use the CNN classifier for printed Arabic OCR using a combination of texture, shape, and statistical features. Evaluation of the proposed method achieves an accuracy of 97.23%, which shows the effectiveness of combining image features with a CNN classifier.…”

Section: Recognitionmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

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Section: Recognitionmentioning

confidence: 99%