Classification of offline gujarati handwritten characters

Intelligent Character Recognition (ICR) is a specific form of optical character recognition (OCR) dealing mostly with handwritten texts. Due to their specificity, they are usually more adept in interpreting different styles and fonts of handwriting providing eventually higher recognition rates. Factors like language constructs, amount of research on ICR pertaining to the language, etc., essentially determines the amount of success achieved in its character recognition. This research mainly deals with the recognition of Gujarati Handwritten Characters. We have considered 34 consonants and 5 vowels; a total of 39 Gujarati Characters. The structure and lexicons of the language posed a challenge during the initial phase of segmentation; for that we have proposed new algorithm for segmentation. Our segmentation algorithm is able to address these concerns effectively. Different algorithms from different domains have been considered for comparative analysis like Transform Domain (DWT, DCT and DFT), from Spatial Domain; Geometric Method (Gradient feature), Structural method (Freeman chain code) and Statistical method (Zernike Moments). We have also proposed a new Combination of Structural and Statistical methods (Freeman chain code, Hu's invariant moment and center of mass) to extract feature vectors and it results into good amount of accuracy. These extracted feature vectors were further supplied as input into Support Vector Machines and their resulting accuracies were analyzed using 10 fold cross validation. SVM performs well on data sets that have many attributes and can also handle large number of classes.