Recognizing arabic handwritten characters using deep learning and genetic algorithms

El-Kady

et al. 2022

Multimed Tools Appl

Self Cite

More than 5% of the people around the world are deaf and have severe difficulties in communicating with normal people according to the World Health Organization (WHO). They face a real challenge to express anything without an interpreter for their signs. Nowadays, there are a lot of studies related to Sign Language Recognition (SLR) that aims to reduce this gap between deaf and normal people as it can replace the need for an interpreter. However, there are a lot of challenges facing the sign recognition systems such as low accuracy, complicated gestures, high-level noise, and the ability to operate under variant circumstances with the ability to generalize or to be locked to such limitations. Hence, many researchers proposed different solutions to overcome these problems. Each language has its signs and it can be very challenging to cover all the languages’ signs. The current study objectives: (i) presenting a dataset of 20 Arabic words, and (ii) proposing a deep learning (DL) architecture by combining convolutional neural network (CNN) and recurrent neural network (RNN). The suggested architecture reported 98% accuracy on the presented dataset. It also reported 93.4% and 98.8% for the top-1 and top-5 accuracies on the UCF-101 dataset.

Section: Introductionmentioning

confidence: 99%

A vision-based deep learning approach for independent-users Arabic sign language interpretation

El-Kady

et al. 2022

Multimed Tools Appl

Self Cite

“…This data was fed to a CNN model which gave an accuracy of 94.9%. Recent works on applying CNN-based deep learning approaches have resulted in better accuracy ( Balaha et al, 2021a ; Balaha et al, 2021b ). Deep learning has also been combined with other approaches such as Mathematical Morphology Operations (MMO) to provide for better character recognition ( Elkhayati, Elkettani & Mourchid, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Writer verification of partially damaged handwritten Arabic documents based on individual character shapes

Khan

Mohammad

Brahim

et al. 2022

PeerJ Computer Science

Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents.

“…The world today is embracing digitizing documents through the adoption of various automated handwritten document recognition techniques ( Balaha et al, 2021 ). In addition, an automated handwritten recognition system takes part in many ancillary applications, such as: educational applications ( Altwaijry & Al-Turaiki, 2021 ), friendly learning environment ( Al-Helali & Mahmoud, 2017 ), bank cheque handling, reading application forms, postal address handling, and handwriting-to-speech transformation ( Ahmed et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, an automated handwritten recognition system takes part in many ancillary applications, such as: educational applications ( Altwaijry & Al-Turaiki, 2021 ), friendly learning environment ( Al-Helali & Mahmoud, 2017 ), bank cheque handling, reading application forms, postal address handling, and handwriting-to-speech transformation ( Ahmed et al, 2020 ). Consequently, handwriting recognition remains an active research area in recent decades and lots of handwriting recognition systems have been introduced to recognize different languages, of which the most common were English ( Yuan et al, 2012 ), Chinese ( Xiao et al, 2017 ; Zhong, Jin & Feng, 2015 ; Bai et al, 2014 ), French ( Xiao et al, 2020 ), Urdu ( Ali et al, 2020 ), and Arabic ( Younis, 2017 ; Shams, Elsonbaty & El Sawy, 2020 ; Balaha et al, 2021 ). Furthermore, different research studies have proposed various intelligent techniques for handwriting recognition using machine learning methods, wherein some research has focused on digits, characters, text, or these elements in combination ( Mudhsh & Almodfer, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent approach for Arabic handwritten letter recognition using convolutional neural network

Ullah

Jamjoom

2022

PeerJ Computer Science

Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature.