Recognition of Amharic Braille

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Addis Ababa University


According to the Il1Iernational E)'e Foundation (IEF) reports, there are currently about45 million visually impaired people in the world, the vast majority of which has been living in Africa In Ethiopia, the latest census indicates that there are well over half a million visually impaired individuals including: studel1ls. lawyers. teachers, researchers, artists etc. So far, Braille has been the most invaluable means for visually impaired individuals to communicate to the world. Braille called after its inventor Braille Luis is a tactile writing means that consists of six dots arrangement in 2-by-3 matrix in a cell. Societal support is important to rebuild lives devastated by sight loss. These days technology has contributed a paramount value 10 the society in facilitating two way communications. Recently, optical character recognition (OCR) technique has been implemented for the recognition of Braille documents scanned with standard scanning device. In this study, an attempt has been made in Amharic Braille-Io-print documents recognition. To achieve this, various techniques has been reviewed, developed and adopted. The proposed system performs the required recognition in two phases: these are recognition of Braille character and Braille-to-print character. The first phase involves four steps such as threes holding/Linearization, image-segmentation, and feature extraction and recognition. Global-threshold has been implemel1led to binaries the foreground (col1len/) from the background. As Braille cell are strictly arranged horizontally and vertically, mesh-grid technique has been adopted for segmentation process. With mesh, dots are extracted following the vertical and horizontal grid line. Having done this, in feature extraction the system once again lakes advantage of the mesh. So, during this sleeps dots are farther grouped in to cell, which would then recognized with context analysis based on rules defined. To accomplish the first phase Microsoft Visual C++ programming tools has been used. The second phase, deals with classification of Braille-to-print. To this end MA TLB 's implemel1lation 0/ The feed forward artificial neural network has been utilized. The neural network classifier has been trained on Amharic Braille with Amharic print as the target character. lV1oreover, the performance of the model has been evaluated with test sets that are prepared .from the Braille document Eventually,, the study has shown better performance with all training and lest set, with 92.5% accurate.



Information Science