Sanskrit word recognition using Prewitt's operator and support vector classification

Abstract

Handwritten recognition has been one of the active and challenging research areas in the field of image processing. In this, paper we are going to proposed to recognize handwritten Sanskrit word using a Prewitt's operator for the edge detection. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts and languages such as Sanskrit has hampered information extraction from a large body of documents of cultural and historical importance. In this we use Freeman chain code(FCC)as the representation technique of an image character. Chain code gives the boundary of a character image in which the codes represents the direction of where is the location of the next pixel. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criteria of features to input the classification is the chain code that converted to various features. And genetic algorithm is applied to evaluate the initial population to find out non-linear segmentation path in the possible segmentation zone. Accordingly, several generations are performed to evaluate the individuals with maximum fitness value. Support vector machine (SVM) is chosen for the classification step.

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

“…To calculate image quality more consistently compared to subjective evaluations, Zhang et al designed a new image quality assessment algorithm that used the Prewitt operator to extract vertical edges in the HSV color space [ 10 ]. Dwivedi et al presented a handwritten Sanskrit word-recognition method using the Prewitt operator for character edge detection [ 11 ]. Nguyen et al built an architecture for the real-time hardware cosimulation of edge detection using the Prewitt edge operator to evaluate the real-time performance of an edge detection algorithm [ 12 ].…”

Recognition of Offline Handwritten Marathi Characters & Numerals Using SVM Classifier

“…The SVM was defined for the two class problem and it looked optimal hyper-plane, which maximized the distance, margin, between the nearest examples of both classes. [9] 4. RESULT We have collected a database of 59 characters & numerals ie combination of 13 vowels,36 consonants,0-9 numbers from individuals of different groups.…”