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All Journal Bulletin of Electrical Engineering and Informatics
Manoj Bangare, Pushpa
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Electrical & Electronics Engineering

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Optimized convolutional neural network deep learning for Arabian handwritten text recognition Ritonga, Mahyudin; L. Bangare, Manoj; Manoj Bangare, Pushpa; L. Bangare, Sunil; Sachin Vanjire, Seema; Moholkar, Kavita; Kasat, Kishori; Rozak, Purnama
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7696

Abstract

In general, the term handwritten character recognition (HCR) refers to the process of recognizing handwritten characters in any form, whereas handwritten text recognition (HTR) refers to the process of reading scanned document images that include text lines and converting those text lines into editable text. The identification of recurring structures and configurations in data is the primary focus of the field of machine learning known as pattern recognition. Optical character recognition, often known as OCR, is a challenging issue to solve when it comes to the field of pattern recognition. This article presents machine learning enabled framework for accurate identification of Arabian handwriting. This framework has provisions for image processing, image segmentation, feature extraction and classification of handwritten images. Images are enhanced using contrast limited adaptive histogram equalization (CLAHE) algorithm. Image segmentation is performed by k-means algorithm. Classification is performed using convolutional neural network (CNN) VGG 16 and support vector machine (SVM) algorithm. Classification accuracy of CNN VGG 16 is 99.33%.
Co-Authors Kasat, Kishori L. Bangare, Manoj L. Bangare, Sunil Mahyudin Ritonga Moholkar, Kavita Purnama Rozak, Purnama Sachin Vanjire, Seema