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