Abstract
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.
Highlights
The recognition of scanned handwritten text is referred as offline handwriting recognition and the recognition of handwriting while writing is called online handwriting recognition
We observed that our system is stable from study observed in experiments section and able to achieve error rate less than 1 % which is at par with the work done in this area
– The computed feature vector is smaller in length as compared to reported work previously and it achieves error rate less than 1 % as happened in good selected results in literature and desired for real-life use applications
Summary
The recognition of scanned handwritten text is referred as offline handwriting recognition and the recognition of handwriting while writing is called online handwriting recognition. Convolution neural networks works as a feature extractor and support vector machine as a recognizer This way the designed hybrid model extracts features from raw images and performs classification. A feature extractor was applied to digit images where number of coefficient of images is computed on learned basis and a local maximum operation is performed [10] This includes support vector machine-based training with proposed feature vector and designed system was compared against techniques as sparse coding, gabor wavelets and principal component analysis. In present study of features from dynamic properties, these features are extracted after recovery of drawing order of digits in first step and computation of its chain codes in second step.
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