A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters

Realization of high accuracies and efficiencies in South Indian character recognition systems is one of the principle goals to be attempted time after time so as to promote the usage of optical character recognition (OCR) for South Indian languages like Telugu. The process of character recognition comprises pre-processing, segmentation, feature extraction, classification and recognition. The feature extraction stage is meant for uniquely recognizing each character image for the purpose of classifying it. The selection of a feature extraction algorithm is very critical and important for any image processing application and mostly of the times it is directly proportional to the type of the image objects that we have to identify. For optical technologies like South Indian OCR, the feature extraction technique plays a very vital role in accuracy of recognition due to the huge character sets. In this work we mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set

Handwritten Script Recognition using DCT, Gabor Filter and Wavelet Features at Line Level

In a country like India where more number of scripts are in use, automatic identification of printed and handwritten script facilitates many important applications including sorting of document images and searching online archives of document images. In this paper, a multiple feature based approach is presented to identify the script type of the collection of handwritten documents. Eight popular Indian scripts are considered here. Features are extracted using Gabor filters, Discrete Cosine Transform, and Wavelets of Daubechies family. Experiments are performed to test the recognition accuracy of the proposed system at line level for bilingual scripts and later extended to trilingual scripts. We have obtained 100% recognition accuracy for bi-scripts at line level. The classification is done using k-nearest neighbour classifier

Directional Discrete Cosine Transform for Handwritten Script Identification

Authors' copy - ICDAR International Conference on Document Analysis and Recognition (2013), Washington DC, USAInternational audienceThis paper presents directional discrete cosine transforms (D-DCT) based word level handwritten script identification. The conventional discrete cosine transform (DCT) emphasizes vertical and horizontal energies of an image and de-emphasizes directional edge information, which of course plays a significant role in shape analysis problem, in particular. Conventional DCT however, is not efficient in characterizing the images where directional edges are dominant. In this paper, we investigate two different methods to capture directional edge information, one by performing 1D-DCT along left and right diagonals of an image, and another by decomposing 2D-DCT coefficients in left and right diagonals. The mean and standard deviations of left and right diagonals of DCT coefficients are computed and are used for the classification of words using linear discriminant analysis (LDA) and K-nearest neighbour (K-NN). We validate the method over 9000 words belonging to six different scripts. The classification of words is performed at bi-scripts, tri-scripts and multi-scripts scenarios and accomplished the identification accuracies respectively as 96.95%, 96.42% and 85.77% in average

Writer Identification for chinese handwriting

Abstract Chinese handwriting identification has become a hot research in pattern recognition and image processing. In this paper, we present overview of relevant papers from the previous related studies until to the recent publications regarding to the Chinese Handwriting Identification. The strength, weaknesses, accurateness and comparison of well known approaches are reviewed, summarized and documented. This paper provides broad spectrum of pattern recognition technology in assisting writer identification tasks, which are at the forefront of forensic and biometrics based on identification application