Enhancing Arabic handwritten word recognition: a CNN-BiLSTM-CTC architecture with attention mechanism and adaptive augmentation

Enhancing Arabic handwritten word recognition: a CNN-BiLSTM-CTC architecture with attention mechanism and adaptive augmentation

Abstract Optical character recognition (OCR) for Arabic presents unique challenges due to the script's cursive nature, contextual letter forms, multiple ligatures, the presence of diacritics, and the high variability in handwritten styles. This work introduces an enhanced Arabic handwritten wor...

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Bibliographic Details
Main Authors: Bounour Imane, Ammour Alae, Khaissidi Ghizlane, Mostafa Mrabti
Format: Article
Language:English
Published: Springer 2025-05-01
Series:Discover Applied Sciences
Subjects:
Arabic handwritten word recognition
Convolutional neural network
Biredirectional long short memory
Conxionnist temporel classification
Word beam search
Attention mechanism
Online Access:https://doi.org/10.1007/s42452-025-06952-z
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https://doi.org/10.1007/s42452-025-06952-z

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