Neural Network Implementation of Character Recognition
| dc.contributor.advisor | Adugna, Eneyew(PhD) | |
| dc.contributor.author | Bireda, Tezazu | |
| dc.date.accessioned | 2018-07-09T12:52:10Z | |
| dc.date.accessioned | 2023-11-28T14:26:36Z | |
| dc.date.available | 2018-07-09T12:52:10Z | |
| dc.date.available | 2023-11-28T14:26:36Z | |
| dc.date.issued | 1998-05 | |
| dc.description.abstract | Artificial neural networks, as they are usually called, currently gained much popularity in the design of "intelligent" machines and in programs which are used for automatic pattern recognition as pattern classifiers. In contrast to symbolic-oriented methods in artificial intelligence (Al), artificial neural networks are computing systems that use mathematical algorithms and "imitate" the way the brain, the biological neural network, functions. They are made up of a number of simple, highly connected non linear processing elements and process information by their dynamic state response to external inputs. They are characterized by the ability to learn and generalize, massive parallelism which gives rise to greater speed on computers with parallel processors or on a dedicated analogue VLSI circuit chip, tolerance to significant erroneous data or network fault, and some models exhibit self organization in the learning phase giving optimum network architecture. Their greatest asset compared to other recognition methods, however, is their ability to learn and generalize. In this paper a feed-forward Back Propagation Network (BPN) architecture, which is one of the several network architectures available, is implemented to recognize printed multifont alpha-numeric (English and Amharic or Geez) characters and its performance is investigated. The network model has three layers and is trained in a supervised training mode. In the research, two independent sets of pattern classes of characters were formed each pattern class having four training and two testing sample character patterns. The first set deals with randomly selected pattern classes and the second set deals with very similar pattern classes. And in both sets of training and testing schemes, the relative recognition performance of the network is evaluated. The network recognition rate or performance, only for the test patterns, in percentage for the first set is about 85% and for the second set is about 67%. The overall recognition rate accounting tests with both the training and test patterns is 93% for the first set and 87% for the second. When the test patterns from these two sets were corrupted with noise, the recognition performance of both sets degraded steadily. Test was also made with tilted test patterns on the first set and the performance was unaffected up to a tilt angle of 4.4 degrees from the vertical. A steady improvement in performance was observed as the dimension of the input pattern vectors, the number of training patterns in a pattern class, and network size were increased. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/7417 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.title | Neural Network Implementation of Character Recognition | en_US |
| dc.type | Thesis | en_US |