Digit Classification Using HOG Features

SKU: PAN_IPM_159 Categories: ,

Description

In this project ,the handwritten digit classification and recognition where digits have to be assigned into one of the 10 classes using some classification method.

Acquire a labled dataset with images of the desired object. To an efficient image appearance feature based approach which process the acquired digit image using Histogram of Oriented Gradients (HOG).

HOG is a very efficient feature descriptor for data discrimination and very stable on illumination variation because it is a gradient based descriptor. For the efficient classification of the HOG features of numeric digits, a linear multiclass Support Vector Machine (SVM) classifier has been proposed, because it has better responses for nonlinear classification cases also.

The proposed system has been evaluated against the Neural Network based classification system.?Experimentation results shows that it has better accuracy interms of misclassification.

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