Prototype Selection for Handwritten Connected Digits Classification

Abstract

After the handwritten segmentation process, it is common to have connected digits. This is due to the great size and shape digit variations. In addition, the acquisition and the binarization processes can add noise to the images. These under segmented images, when given as input to classifiers which are specialists to deal with digits separately, should lead to errors. Aiming to detect the handwritten connected digits, it is herein introduced a hybrid system architecture to be used as a segmentation pos-processing task. The proposed system is based on a prototype selection scheme that combines self-generating prototypes and Gaussian mixtures. Besides, this work presents a set of features for the proposed problem. A real-world database of handwritten digits was used to validate the new approach. The results obtained in the experimental study showed that the hybrid strategy achieved promising accuracy rates.

A Statistical Approach For Latin Handwritten Digit Recognition

“…There are several approaches for handwritten digit recognition problem have been reported in the literature in the past. They include SVM (support vector machine) [1,2,3], NN (neural network) [4,5,6,7], deformable template matching [8,9], hybrid method [10,11,12,13] and others. In SVMs for digit classification problems, the training of a large data set is still a bottle-neck and is comparatively slow.…”