Recognition Impact on Rescaled Handwritten Digit Images Using Support Vector Machine Classification
Abstract?Handwritten Digit Recognition has been proposed using different techniques that were implemented over the available datasets. Although existing systems reached high recognition accuracy, more efforts regarding speed and memory allocation is required. In this research, we experiment the impact of image resolution reduction on recognition accuracy for handwritten digits. A set of features were extracted, include histogram of pixels for horizontal, vertical, diagonal and inversed diagonal orientations. Feature vector constructed by joining these features. Then, support vector machine is applied for classification. Rescaled handwritten digit images were experimented against recognition accuracy, speed and memory. MNIST database of handwritten digits is utilized for implementation. Results showed that the reduction of the size for the features vector due to image rescaling to quarter of the original size had only about 1% accuracy degradation impact.
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