Off-line Handwritten Character Recognition System Research Articles

Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

Offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

Role of Offline Handwritten Character Recognition System in Various Applications

Offline handwritten character recognition system recognizes characters from images. Since the past few decades, many researchers have been developing various handwritten recognition systems for various languages. This paper demonstrates the role and significance of an offline handwritten character recognition system in various applications. General Terms Offline Handwritten Recognition System, Pattern Recognition, Image Processing

PCA-based Offline Handwritten Character Recognition System

Principal component analysis (PCA) has been used widely in pattern recognition to reduce the extent of the data. In this paper, we explore using this technique to recognize offline handwritten Gurmukhi characters, and a system for offline handwritten Gurmukhi character recognition using PCA is proposed. The system first prepares a skeleton of the character so that meaningful feature information about the character can be extracted. For classification, we used k-nearest neighbor, Linear-SVM, polynomial-SVM and RBF-SVM based approaches and combinations of these approaches. In this work, we collected 16,800 samples of isolated offline handwritten Gurmukhi characters. These samples were divided into three categories. In category 1 (5600 samples), each Gurmukhi character was written 100 times by a single writer. In category 2 (5600 samples), each Gurmukhi character was written 10 times by 10 different writers, and in category 3 (5600 samples), each Gurmukhi character was written by 100 different writers. The set of the basic 35 akhars of Gurmukhi has been considered here. A partitioning strategy for selecting the training and testing patterns is also explored in this work. We used zoning, diagonal, directional, transition, intersection and open end point, parabola curve fitting?based and power curve fitting?based feature extraction in order to find the feature set for a given character. The proposed system achieves a recognition accuracy of 99.06% in category 1, 98.73% in category 2 and 78.30% in category 3.