Online Handwriting Recognition of Ethiopic Script

Online recognition of handwritten characters is gaining a renewed interest as it provides a natural way of data entry for a wide variety of handheld devices. In this paper, we present online handwriting recognition system for Ethiopic script based on the structural and syntactical analysis of the strokes forming characters. The complex structures of characters are represented by the spatiotemporal relationships of simple-shaped strokes called primitives. A special tree structure is used to model spatiotemporal relationships of the strokes. The tree generates a unique set of primitive stroke sequences for each character, and for recognition each stroke sequence is matched against a stored knowledge base. Characters are also classified based on their structural similarity to select a plausible set of characters for un unknown input, which improves recognition and processing time. We also present a dataset collected for training and testing online recognition systems for Ethiopic script. The dataset is prepared in accordance with the international standard UNIPEN format. The recognition system is tested with the collected dataset and experimental results are reported.