Digital hardware implementation of Self-Organising Maps

In this paper a digital hardware implementation of the Self-Organising Maps (SOMs) for the application of handwritten digit recognition is presented. Two methods were implemented: Euclidean and Manhattan method. The highest recognition rate for both methods was calculated through three testing techn...

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Bibliographic Details
Main Authors: Cutajar, M, Gatt, E, Micallef, J, Grech, I, Casha, O
Format: Conference Proceeding
Language:English
Subjects:
Handwriting recognition
Hardware
Microelectronics
Neural networks
Neurofeedback
Neurons
Pattern recognition
Testing
Trade agreements
Writing
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Summary:In this paper a digital hardware implementation of the Self-Organising Maps (SOMs) for the application of handwritten digit recognition is presented. Two methods were implemented: Euclidean and Manhattan method. The highest recognition rate for both methods was calculated through three testing techniques. The highest recognition rates obtained are 71.267% and 63.667% for the Euclidean and the Manhattan methods respectively. Both methods were implemented on the Xilinx Spartan-3 200K gates (XC3S200) to compare their speed performance and area consumed.
ISSN:2158-8473
2158-8481
DOI:10.1109/MELCON.2010.5476361