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Implementation of FPGA-based artificial neural network for character recognition

Master of Science (Embedded System Design Engineering)

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Bibliographic Details
Main Author: Omar Sadeq, Salman
Other Authors: Dr. Phaklen Ehkan
Format: Other
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2019
Subjects:
Neural networks (Computer Science)
Artificial Neural Networks (ANN)
Neural computation
Optical character recognition (OCR)
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63449
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spelling my.unimap-634492019-11-29T06:54:13Z Implementation of FPGA-based artificial neural network for character recognition Omar Sadeq, Salman Dr. Phaklen Ehkan Neural networks (Computer Science) Artificial Neural Networks (ANN) Neural computation Optical character recognition (OCR) Master of Science (Embedded System Design Engineering) Artificial Neural Networks (ANN) are non-linear applied math knowledge data modeling tools, usually used model advanced relationships between inputs and outputs or to seek out patterns in data. A generic hardware primarily based ANN is planned and executed using VHDL coding. This project may be seen as a place to begin for learning ANN. It explores in approach a hardware-based application of ANN employs FPGA. The sixteen toggle switches are given as input while the end product is exhibited on the LCD display. This classifier is trained to identify letters on a 4x4 binary grid filled by a user through 16 toggle switches. The most probable class suggested by the ANN is displayed on an LCD screen. To demonstrate the practicality of FPGA execute of ANN, the ANN trained to acknowledge twenty English and nine Arabic character patterns on a 4x4 grid. In structural of ANN, the used of three-layer is implemented entirely with 32-bit single exactitude floating purpose arithmetic to ensure flexibility and accuracy for its wide selection of applications. The resulting design file is programmed into the Altera Cyclone II FPGA on the Altera DE2 development and education board. The design also includes a training supervisor that trains the ANN recognized the total of 29 English and Arabic alphabet predefined characters. The result is promising as the ANN is able to recognize all characters defined to training characters patterns. Each alphabet is tested in 20 English alphabet and 9 Arabic alphabet, after implementation, are done and performance issues of the design are analyzed. The output gives good results, and finally this project shows the flexibility and also the endless chance of hardware primarily based implementation of ANN , the achievement of recognition rate for alphabet English and Arabic character are 76.92% and 32.14% respectively. 2019-11-29T06:54:13Z 2019-11-29T06:54:13Z 2014 Other http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63449 en Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Neural networks (Computer Science)
Artificial Neural Networks (ANN)
Neural computation
Optical character recognition (OCR)
spellingShingle Neural networks (Computer Science)
Artificial Neural Networks (ANN)
Neural computation
Optical character recognition (OCR)
Omar Sadeq, Salman
Implementation of FPGA-based artificial neural network for character recognition
description Master of Science (Embedded System Design Engineering)
author2 Dr. Phaklen Ehkan
author_facet Dr. Phaklen Ehkan
Omar Sadeq, Salman
format Other
author Omar Sadeq, Salman
author_sort Omar Sadeq, Salman
title Implementation of FPGA-based artificial neural network for character recognition
title_short Implementation of FPGA-based artificial neural network for character recognition
title_full Implementation of FPGA-based artificial neural network for character recognition
title_fullStr Implementation of FPGA-based artificial neural network for character recognition
title_full_unstemmed Implementation of FPGA-based artificial neural network for character recognition
title_sort implementation of fpga-based artificial neural network for character recognition
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2019
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63449
_version_ 1651868847940042752
score 13.160551

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