Feature Extraction Technique For Recognition Research Articles

Offline handwritten pre-segmented character recognition of Gurmukhi script

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offline handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classifiers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

A combined static and dynamic feature extraction technique to recognize handwritten digits

The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (\(<\)1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words. General terms Speech Recognition, Recognition rate, Bit-wise, Zero crossing rate.