Isolated Handwritten Digit Recognition using Adaptive Unsupervised Incremental Learning Technique

Abstract

This paper presents a new approach to off-line handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. The recognition of handwritten numerals is a challenging task in the field of image processing and pattern recognition. It can be considered as one of the benchmarks in evaluating feature extraction methods and the performance of classifiers. The performance of character recognition system depends heavily on what kind of features are being used. The objective of this paper is to provide efficient and reliable techniques for recognition of handwritten numerals. In this paper we propose Zoning based feature extraction system which calculates the densities of object pixels in each zone. Firstly the whole image is divided into 4 4 zones. Further in order to gain more accuracy these zones are divided into 6 6 zones. The division of zones carried out up to 8 8 zones. Hence 116 features are extracted in all. Nearest neighbour classifier is used for subsequent classification and recognition purpose.

Handwritten Digit Recognition from Digital Image

“…Processing and Neural Network: At the point when the reasonable structures have been physically filled by different people by then channel these structures with the help of scanner. So now we have pictures of hand making tries out of digits [2]. By and by possible to recognize the significance of any physically composed digit with the help of AI engine.…”

“…By and by possible to recognize the significance of any physically composed digit with the help of AI engine. [2] So now at whatever point any physically composed digit will be given as test commitment to the structure, the yield show will normally give the digit who's relating match regard is recognized. The above strategy is a diagram of human abstract thinking system.…”

“…In segment 1 and store the digit ID suppose any digit from ( 1 to 10 )So we read all the 1000 pictures recently cut and put away one by one and store the worldwide histogram In these segment for digits 0 to 9. [2] 94 input neurons in the info layer: 15 shrouded neurons in the concealed layer 10 yield neurons: yield layer which Correspond to digits 0-9.Input Neurons Input required for 94 input neurons is perused from the p4 components of the worldwide histogram. Out-put Neurons 10 yield neurons recommend the relating identification of digits from (1 to 10) [2].…”

“…[2] 94 input neurons in the info layer: 15 shrouded neurons in the concealed layer 10 yield neurons: yield layer which Correspond to digits 0-9.Input Neurons Input required for 94 input neurons is perused from the p4 components of the worldwide histogram. Out-put Neurons 10 yield neurons recommend the relating identification of digits from (1 to 10) [2]. When the neural system has been prepared for each of the ten digits now it is conceivable to recognize the importance of any written by hand digit with the assistance of the prepared neural system [2].…”

Assamese Digit Recognition with Feed Forward Neural Network

“…Sharma and Gupta [8] used 4x4 zone density of pixel as features and a k-nn classifier with k=1 for classification. They have used 10,000 and 5400 training and test patterns respectively.…”