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Recognizing Arabic Handwritten Characters Using Deep Learning and Genetic Algorithms by Hossam Magdy Balaha; Hesham Arafat Ali; Esraa Khaled Youssef; Asmaa Elsayed Elsayed; Reem Adel Samak; Mohammed Samy Abdelhaleem; Mohammed Mosa Tolba; Mahmoud Ragab Shehata; Mahmoud Refa’at Mahmoud; Mariam Mahmoud Abdelhameed; Mostafa Mahmoud Mohammed is a Computer Science article available to read on EtoBox.

What is Recognizing Arabic Handwritten Characters Using Deep Learning and Genetic Algorithms about?

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country's history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN

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Author

Hossam Magdy Balaha; Hesham Arafat Ali; Esraa Khaled Youssef; Asmaa Elsayed Elsayed; Reem Adel Samak; Mohammed Samy Abdelhaleem; Mohammed Mosa Tolba; Mahmoud Ragab Shehata; Mahmoud Refa’at Mahmoud; Mariam Mahmoud Abdelhameed; Mostafa Mahmoud Mohammed

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)