Implementation of FPGA-based artificial neural network for character recognition

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 see...

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Main Author:	Omar Sadeq, Salman
Format:	Thesis
Language:	English
Subjects:	Artificial Neural Networks (ANN) Character recognition Back Propagation (BP) Field Programmable Gate Arrays (FPGA) Optical character recognition (OCR)
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/2/Full%20text.pdf
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spelling	my-unimap-594252019-04-10T04:26:12Z Implementation of FPGA-based artificial neural network for character recognition Omar Sadeq, Salman Dr. Phaklen Ehkan 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. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/59425 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/1/Page%201-24.pdf 3b32eac7f1f024a15a5cd36e19ee1abb http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/2/Full%20text.pdf 01703d6a408758b965d870556cf3e174 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Artificial Neural Networks (ANN) Character recognition Back Propagation (BP) Field Programmable Gate Arrays (FPGA) Optical character recognition (OCR) School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Phaklen Ehkan
topic	Artificial Neural Networks (ANN) Character recognition Back Propagation (BP) Field Programmable Gate Arrays (FPGA) Optical character recognition (OCR)
spellingShingle	Artificial Neural Networks (ANN) Character recognition Back Propagation (BP) Field Programmable Gate Arrays (FPGA) Optical character recognition (OCR) Omar Sadeq, Salman Implementation of FPGA-based artificial neural network for character recognition
description	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.
format	Thesis
author	Omar Sadeq, Salman
author_facet	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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/59425/2/Full%20text.pdf
_version_	1747836836679516160

Implementation of FPGA-based artificial neural network for character recognition

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