Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition
Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to a...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2024-01-01
|
| Series: | International Journal of Data and Network Science |
| Online Access: | http://www.growingscience.com/ijds/Vol8/ijdns_2024_57.pdf |
| _version_ | 1797201532991569920 |
|---|---|
| author | Husam Ahmad Al Hamad Mohammad Shehab |
| author_facet | Husam Ahmad Al Hamad Mohammad Shehab |
| author_sort | Husam Ahmad Al Hamad |
| collection | DOAJ |
| description | Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer Perceptron (MLP) is one of the most common Artificial Neural Networks (ANNs) which deals with various problems efficiently. Therefore, this study introduces a new technique called Block Density and Location Feature (BDLF) with MLP, namely BDLF-MLP, which aims to extract novel features from letter images and estimate the letter's pixel density and its location for each equal-sized block in the image. In other words, BDLF-MLP can deal with various styles of Arabic handwritten, such as overlapping letters. The BDLF-MLP starts with the Block Feature Extraction (BFE) of the image by dividing the image into sixteen parts. After that, it calculates the density and location of each block (i.e., BDLF) by finding the sum of all values inside blocks. Finally, it determines the position of the greatest pixel density to obtain better recognition accuracy. The dataset containing 720 images is used to evaluate the efficiency of the proposed technique. Also, 1440 letters are used for training and testing divided evenly between them. The experiment results illustrate that BDLF-MLP outperformed the other algorithms in the literature with an accuracy of 97.26 %. |
| first_indexed | 2024-04-24T07:49:03Z |
| format | Article |
| id | doaj.art-8d4e574aa5ca4ddea76708830fd3998f |
| institution | Directory Open Access Journal |
| issn | 2561-8148 2561-8156 |
| language | English |
| last_indexed | 2024-04-24T07:49:03Z |
| publishDate | 2024-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj.art-8d4e574aa5ca4ddea76708830fd3998f2024-04-18T15:10:24ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562024-01-01831501151610.5267/j.ijdns.2024.3.015Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognitionHusam Ahmad Al HamadMohammad Shehab Arabic handwritten script recognition presents an energetic area of study. These types of recognitions face several obstacles, such as vast open databases, boundless diversity in individuals' penmanship, and freestyle writing. Thus, Arabic handwriting requires effective techniques to achieve better recognition results. On the other hand, Multilayer Perceptron (MLP) is one of the most common Artificial Neural Networks (ANNs) which deals with various problems efficiently. Therefore, this study introduces a new technique called Block Density and Location Feature (BDLF) with MLP, namely BDLF-MLP, which aims to extract novel features from letter images and estimate the letter's pixel density and its location for each equal-sized block in the image. In other words, BDLF-MLP can deal with various styles of Arabic handwritten, such as overlapping letters. The BDLF-MLP starts with the Block Feature Extraction (BFE) of the image by dividing the image into sixteen parts. After that, it calculates the density and location of each block (i.e., BDLF) by finding the sum of all values inside blocks. Finally, it determines the position of the greatest pixel density to obtain better recognition accuracy. The dataset containing 720 images is used to evaluate the efficiency of the proposed technique. Also, 1440 letters are used for training and testing divided evenly between them. The experiment results illustrate that BDLF-MLP outperformed the other algorithms in the literature with an accuracy of 97.26 %.http://www.growingscience.com/ijds/Vol8/ijdns_2024_57.pdf |
| spellingShingle | Husam Ahmad Al Hamad Mohammad Shehab Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition International Journal of Data and Network Science |
| title | Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition |
| title_full | Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition |
| title_fullStr | Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition |
| title_full_unstemmed | Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition |
| title_short | Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition |
| title_sort | integrated multi layer perceptron neural network and novel feature extraction for handwritten arabic recognition |
| url | http://www.growingscience.com/ijds/Vol8/ijdns_2024_57.pdf |
| work_keys_str_mv | AT husamahmadalhamad integratedmultilayerperceptronneuralnetworkandnovelfeatureextractionforhandwrittenarabicrecognition AT mohammadshehab integratedmultilayerperceptronneuralnetworkandnovelfeatureextractionforhandwrittenarabicrecognition |