Oriya off-line handwritten character recognition

Recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Oriya handwritten characters using quadratic classifier based on the features obtained mainly from directional chain code information. Here, at first, the bounding box of a character is segmented into blocks and directional chain code features are computed in each of these blocks. Next, these blocks are down sampled using Gaussian filter for recognition. Finally, chain code features obtained from down sampled blocks are fed to the quadratic classifier for recognition. We used two sets of feature vectors (one feature vector has 64 dimension and the other has 400 dimension) and their corresponding results obtained from the classifier are reported. We tested our system on 9556 Oriya off-line handwritten characters and we obtained 91.11% accuracy from the proposed recognition system when 400 dimensional feature vector was considered. We have used five-fold cross-validation technique for result computation.