Stroke identification in Gujarati text using directional feature

Strokes are the most natural way of describing the character formation; although most of the researcher uses transform based features for recognition of offline text. The stroke base features are very popular in online handwritten text recognition because it is easy to identify stroke and its sequence by tracing pen tip, whereas it is difficult to obtain the same information in the offline text. The aim of this research is to propose the method which will separate the strokes from the thinned binary image of text and extract the directional features from the separated stroke. The strokes are further categorized using k-Nearest Neighbor (k-NN). Recognition of printed Gujarati numeral is selected as a case study to validate proposed features. Accuracy obtains at stroke level is 88%, however the accuracy at symbolic level is likely to improve since every symbol is a collection of multiple stroke. Since there is no standard data set available exclusively for Gujarati text, so this research also aims to generate a dataset of isolated Gujarati numeral.