Handwritten Semitic Language Digit Recognition Using Deep Learning

Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Amharic script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Amharic digit recognition researches are very few. Convolutional Neural Network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this thesis, the proposed model is to recognize Amharic digits using CNN. In order to recognize handwritten Amharic digits a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning applications, but has not been endeavored for Ethiopic script.