Holistic Approach for Arabic Word Recognition

Wahbi, Talaat M.; Musa, Mohamed E. M.

doi:10.7753/ijcatr0503.1005

Cited by 3 publications

(1 citation statement)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e datasets used here are the large datasets that are stored on the cloud. ere are some other examples where authors have implemented only a holistic approach for the recognition of various scripts like English, Bangla, and Arabic and achieved an accuracy of 90.3%, 83.64%, and 63% [11][12][13]. Manchala et al [9] have used NN for the recognition of English script using the Holistic approach.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Sharma

Gupta

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

The challenges involved in the traditional cloud computing paradigms have prompted the development of architectures for the next generation cloud computing. The new cloud computing architectures can generate and handle huge amount of data, which was not possible to handle with the help of traditional architectures. Deep learning algorithms have the ability to process this huge amount of data and, thus, can now solve the problem of the next generation computing algorithms. Therefore, these days, deep learning has become the state-of-the-art approach for solving various tasks and most importantly in the field of recognition. In this work, recognition of city names is proposed. Recognition of handwritten city names is one of the potential research application areas in the field of postal automation For recognition using a segmentation-free approach (Holistic approach). This proposed work demystifies the role of convolutional neural network (CNN), which is one of the methods of deep learning technique. Proposed CNN model is trained, validated, and analyzed using Adam and stochastic gradient descent (SGD) optimizer with a batch size of 2, 4, and 8 and learning rate (LR) of 0.001, 0.01, and 0.1. The model is trained and validated on 10 different classes of the handwritten city names written in Gurmukhi script, where each class has 400 samples. Our analysis shows that the CNN model, using an Adam optimizer, batch size of 4, and a LR of 0.001, has achieved the best average validation accuracy of 99.13.

show abstract

Section: Related Workmentioning

confidence: 99%

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Sharma

Gupta

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network

Kaur

Bansal²,

Kumar

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

A Deep Learning Approach for Handwritten Arabic Names Recognition

Mustafa¹,

Khalafallah²

2020

IJACSA

View full text Add to dashboard Cite

Optical Character recognition (OCR) has enabled many applications as it has attained high accuracy for all printing documents and also for handwriting of many languages. However, the state-of-the-art accuracy of Arabic handwritten word recognition is far behind. Arabic script is cursive (both printed and handwritten). Therefore, traditionally Arabic recognition systems segment a word to characters first before recognizing its characters. Arabic word segmentation is very difficult because Arabic letters contain many dots. Moreover, Arabic letters are context sensitive and some letters overlapped vertically. A holistic recognizer that recognizes common words directly (without segmentation) seems the plausible model for recognizing Arabic common words. This paper presents the result of training a Conventional Neural Network (CNN), holistically, to recognize Arabic names. Experiments result shows that the proposed CNN is distinct and significantly superior to other recognizers that were used with the same dataset.

show abstract

Holistic Approach for Arabic Word Recognition

Cited by 3 publications

References 7 publications

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Recognition of Gurmukhi Handwritten City Names Using Deep Learning and Cloud Computing

Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network

A Deep Learning Approach for Handwritten Arabic Names Recognition

Contact Info

Product

Resources

About