Handwritten Digit Recognition (HDR) is one of the most challenging tasks in domain Optical Character (OCR). Irrespective language, there are some inherent challenges HDR, which mostly arise due to variations writing styles across individuals, medium and environment, inability maintain same strokes while any digit repeatedly, etc. In addition that, structural complexities digits a particular lan...

This paper presents a study on the performance of transformed domain features in Devnagari digit recognition. In this research the recognition performance is measured from features obtained in direct pixel value, Fourier Transform, Discrete Cosine Transform, Gaussian Pyramid, Laplacian Pyramid, Wavelet Transform and Curvelet Transform using classification schemes: Feed Forward, Function Fitting...

The author previously proposed a neural network model neocognitron for robust visual pattern recognition. This paper proposes an improved version of the neocognitron and demonstrates its ability using a large database of handwritten digits (ETL1). To improve the recognition rate of the neocognitron, several modi0cations have been applied: such as, the inhibitory surround in the connections from...

Purpose The amount of features in handwritten digit data is often very large due to the different aspects personal handwriting, leading high-dimensional data. Therefore, employment a feature selection algorithm becomes crucial for successful classification modeling, because inclusion irrelevant or redundant can mislead modeling algorithms, resulting overfitting and decrease efficiency. Design/m...