Teferi, Dereje (PhD)Abebe, Sertse2018-11-302023-11-292018-11-302023-11-292011-06http://etd.aau.edu.et/handle/123456789/14739OCR is a type of document image analysis techniques to recognize the informative content in the text documents to be archived in softcopy for different purposes. The technique involves in conversion of the given image of text to its most probable similar character in a given domain language scripts. A line of a multilingual document page may contain text words in different languages. To recognize, such a document page, it is necessary to identify different script forms before running an individual OCR system. In this paper, a system that distinctly identifies Amharic and English Scripts from a document image is presented. The system addresses the language identification problem on the word level. In extracting the important feature values of word-image of the scripts, preprocessing activities such as noise removal, binarization, segmentation, size and style normalization activities are performed. Maximum Horizontal Projection profiles from three selected region, extent of the word image, and the ratio of the number of connected component to the word-image width are the important feature value to discriminate the two languages script. Support Vector Machine algorithm is applied to classify new instance word images. The proposed algorithm is tested with significant number of words with various font styles and sizes. The results obtained are quite promising and encouragingenBilingual Script IdentificationThesis