Open AccessUse an Efficient Neural Network to Improve the Arabic Handwriting Recognition
Author(s) -
Husam Ahmed Al Hamad
Publication year - 2021
Publication title -
international journal of neural networks and advanced applications
Language(s) - English
Resource type - Journals
ISSN - 2313-0563
DOI - 10.46300/91016.2021.8.5
Subject(s) - artificial neural network , computer science , segmentation , artificial intelligence , pattern recognition (psychology) , scripting language , handwriting , handwriting recognition , arabic , machine learning , speech recognition , feature extraction , linguistics , philosophy , operating system
Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.