Line and word segmentation of Kannada handwritten text documents using projection profile technique
Historic handwritten documents are valuable traditional legacy, as they deliver insights into both tangible and intangible cultural features since the ancient time. Handwritten historical documents stance real experiments for involuntary processing, such as image binarization and segmentation. Segmentation of handwritten pages interested in text lines and words is problematic as the script is not unique and the lettering differs a lot contingent on the writer and yet, it is a significant phase for the recognition of content. In this paper we have presented a segmentation system based on Projection Profile method for general handwritten text and historical handwritten text. The system includes procedures for preprocessing, the preprocessing of text image consists of noise removal, binarization, skew detection and correction, following these preprocessing steps, the text image is split into individual text lines then these text lines to individual words. The proposed algorithm provides 90% successful segmentation achieved in line segmentation and but word segmentation level needs more effort as it is complicated for Kannada script. The accuracy is reduced due to lack of space between the text lines and overlapping of subscripts.
2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)