Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification

Asma ElAdel; Ridha Ejbali; Mourad Zaied; Chokri Ben Amar; IEEE

doi:10.1109/ICTAI.2015.119

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Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification

Asma ElAdel, Ridha Ejbali, Mourad Zaied, Chokri Ben Amar and IEEE

2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vol.2016-, pp.807-812

01/11/2015

DOI: https://doi.org/10.1109/ICTAI.2015.119

Abstract

Adaboost

Arabic handwritten character

Biological neural networks

Character recognition

classification

deep learning

dyadic wavelet

Feature extraction

Hidden Markov models

Machine learning

Multiresolution analysis

Shape

The problem addressed in this paper is the classification and recognition of Arabic handwritten characters. As a solution, we present a Neural Network (NN) architecture based on Fast Wavelet Transform (FWT) and Adaboost algorithm. FWT is used to extract character's features, based on Multi-Resolution Analysis (MRA) at different levels of abstraction. These features are used to calculate inputs of hidden layer. After this first step, the features are filtered, using Adaboost algorithm, to select the best corresponding ones to each shape of input characters. The reported results are tested on Arabic handwritten characters dataset with 6000 characters. The classification rate for the different groups of characters are 93.92%. Additionally, the speed of the classification algorithm is tested and reported.

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Details

Title: Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification
Creators - without role: Asma ElAdel - University of Sfax
Ridha Ejbali - University of Sfax
Mourad Zaied - University of Sfax
Chokri Ben Amar - University of Sfax
IEEE
Publication Details: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vol.2016-, pp.807-812
Publisher: IEEE
Identifiers: 9910593908331
Academic Unit: Taif University
Language: English
Resource Type: Conference proceeding