Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts

Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts

Recognition of cursive handwritten Arabic text is a difficult problem because of context-sensitive character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. In this paper, we rev...

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Bibliographic Details
Main Authors: Mohamed Eltay, Abdelmalek Zidouri, Irfan Ahmad
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Arabic handwriting recognition (AHR)
deep learning neural network (DLNN)
convolutional neural networks (CNN)
connectionist temporal classification (CTC)
recurrent neural network (RNN)
IFN/ENIT database
Online Access:https://ieeexplore.ieee.org/document/9091836/

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