Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

Abstract

In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, (x,y) (2 x,2 y) and the global features like tan( ) are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan ( ) Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database.