Novel feature extraction technique for the recognition of handwritten digits

A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters

High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two differe...

Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

Handwritten Digits Recognition

Recognition of handwritten digits using sparse codes generated by local feature extraction methods

We investigate when sparse coding of sensory inputs can improve performance in a classification task. For this purpose, we use a standard data set, the MNIST database of handwritten digits. We systematically study combinations of sparse coding methods and neural classifiers in a two-layer network. We find that processing the image data into a sparse code can indeed improve the classification pe...

A Novel Feature Extraction Technique for Facial Expression Recognition

This paper presents a new technique to extract the light invariant local feature for facial expression recognition. It is not only robust to monotonic gray-scale changes caused by light variations but also very simple to perform which makes it possible for analyzing images in challenging real-time settings. The local feature for a pixel is computed by finding the direction of the neighboring of...