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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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| creator | Cutajar, M Gatt, E Micallef, J Grech, I Casha, O |
| description | 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. |
| doi_str_mv | 10.1109/MELCON.2010.5476361 |
| format | conference_proceeding |
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| identifier | ISSN: 2158-8473 |
| ispartof | Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference, 2010, p.1123-1128 |
| issn | 2158-8473 2158-8481 |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Handwriting recognition Hardware Microelectronics Neural networks Neurofeedback Neurons Pattern recognition Testing Trade agreements Writing |
| title | Digital hardware implementation of Self-Organising Maps |
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