Line Fitting to Amharic OCR: the Case of Postal Address
| dc.contributor.advisor | Teferi, Dereje | |
| dc.contributor.advisor | Taddesse, Nigussie | |
| dc.contributor.advisor | Assabe, Yaregal | |
| dc.contributor.author | Hailemariam, Mesay | |
| dc.date.accessioned | 2018-11-27T13:40:45Z | |
| dc.date.accessioned | 2023-11-18T12:44:05Z | |
| dc.date.available | 2018-11-27T13:40:45Z | |
| dc.date.available | 2023-11-18T12:44:05Z | |
| dc.date.issued | 2003-07 | |
| dc.description.abstract | Currently researchers are attracted to the area of Optical Character recognition primarily due to challenging nature of the research and secondly due to the industrial importance that it provides in the area of Reading machine for the Blind, postal Address interpretation, Bank Curtsey amount processing, hand filled form processing, and the like. Research in the area of Amharic OCR systems is ongoing since 1997. Attempts were made in adopting algorithm to Amharic language, incorporating preprocessing techniques to the adopted algorithm, and in generalizing the system so as it recognizes Type written characters as well as hand written characters. Sufficient amount of work is done in the areas of preprocessing such as segmentation and Noise Removal. However, the consideration given to the simplification of the feature extraction and the efforts made to alleviate the problems of high dimensional input still requires the contribution of many additional researches in order to come up with a system that the society can use to solve real world problems. To this end, Line fitting is used to Amharic Optical character recognition by applying simple geometric calculations to determine features which could represent and describe the character as uniquely and precisely as possible. The image of a segmented character which is normalized into 32x32 pixels is divided into 16 smaller squares of 8x8 pixels. Then the least square technique was applied to fit a linear model to the distribution of foreground pixels and three features were extracted from each smaller square. Finally, a feed forward Neural Network trained using a back propagation algorithm is used on handwriting of three individuals using a cross validation technique as well as a separate test set and results are depicted on tables and confusion matrices. Relevant Conclusions were drawn and some valid recommendations were forwarded to indicate future direction of further works on the area. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/14565 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Character recognition | en_US |
| dc.title | Line Fitting to Amharic OCR: the Case of Postal Address | en_US |
| dc.type | Thesis | en_US |