Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
| Autor: | Bassam Naji Al-Tamimi, Mandar Gogate, Kia Dashtipour, Ahsen Tahir, Amir Hussain, Mohammed Ahmed El-Affendi, Ahmad Hawalah, Rami Ahmed |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
databases
Computer science Feature extraction General Physics and Astronomy 02 engineering and technology Overfitting Document processing computer.software_genre dropout Convolutional neural network Article Arabic handwritten 0202 electrical engineering electronic engineering information engineering Arabic script business.industry Deep learning 020206 networking & telecommunications batch normalization Pattern recognition (psychology) 020201 artificial intelligence & image processing DCNN Artificial intelligence business computer MNIST database Natural language processing |
| Zdroj: | Entropy |
| Popis: | Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we propose a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters. This aims to prevent overfitting and further enhance generalization performance when compared to conventional deep learning models. We employ a number of deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The model is extensively evaluated and shown to demonstrate excellent classification accuracy when compared to conventional OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). A further experimental study is conducted on the benchmark Arabic databases by exploiting transfer learning (TL)-based feature extraction which demonstrates the superiority of our proposed model in relation to state-of-the-art VGGNet-19 and MobileNet pre-trained models. Finally, experiments are conducted to assess comparative generalization capabilities of the models using another language database , specifically the benchmark MNIST English isolated Digits database, which further confirm the superiority of our proposed DCNN model. |
| Databáze: | OpenAIRE |
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