Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals
This work addresses the recognition of handwritten numerals, which is an incremental improvement in the domain of character recognition.
The paper tackled the problem of recognizing isolated handwritten Roman numerals by designing a novel convex hull-based feature set, achieving a maximum success rate of 97.44% on the MNIST test dataset using an MLP classifier.
In this paper, convex hull based features are used for recognition of isolated Roman numerals using a Multi Layer Perceptron (MLP) based classifier. Experiments of convex hull based features for handwritten character recognition are few in numbers. Convex hull of a pattern and the centroid of the convex hull both are affine invariant attributes. In this work, 25 features are extracted based on different bays attributes of the convex hull of the digit patterns. Then these patterns are divided into four sub-images with respect to the centroid of the convex hull boundary. From each such sub-image 25 bays features are also calculated. In all 125 convex hull based features are extracted for each numeric digit patterns under the current experiment. The performance of the designed feature set is tested on the standard MNIST data set, consisting of 60000 training and 10000 test images of handwritten Roman using an MLP based classifier a maximum success rate of 97.44% is achieved on the test data.