Diagonal Feature Extraction Based Handwritten Character System Using Neural Network

Abstract

A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form.

A Novel Approach Towards Online Devnagari Handwritten Word Recognition Based on Robust Feature Extraction Method and FFNN Classifier.

“…Three different orientations, namely, horizontal, vertical and diagonal directions are used to extract 54 features from each character. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy [22]. Brijmohan Singh, used two different methods for extracting features from handwritten Devnagari characters, the Curvelet Transform and the Character Geometry, and compares their recognition performances using two different classifiers, viz., the Support Vector Machine with Radial Basis Function and the k-Nearest Neighbour classifier [23].Mahesh Jangid have proposed the method for feature extraction like Zonal density, Projection histogram, Distance Profiles, Background Directional Distribution and SVM for classification and they have got 98%, 99.1% and 99.2% of accuracy [24].…”

Efficient Feature Extraction Techniques for Offline Handwritten Gurmukhi Character Recognition

“…Sharma et al [9] have proposed a directional chain code features based quadratic classifier and obtained 80.36 % accuracy for handwritten Devanagari characters. Pardeep et al [10] have used diagonal feature extraction technique for handwritten character recognition system. Rajput and Mali [11] have reported chain code features for Marathi handwritten numeral recognition and they achieved 98.15 % recognition accuracy.…”

Arabic Numerals Recognition based on an Improved Version of the Loci Characteristic

“…We have been limited, in this work to one hidden layer, this have shown to be sufficient after many simulations carried out (figure3). The number of neurons in the hidden layer has generally been determined heuristically or by trial and error [17,21,22]. The input layer has n 1 neurons that we call ne k , 1≤k≤n 1 , the hidden and output layers contain n 2 and n 3 neurons that we respectively call nc j and ns i , where 1≤j≤n 2 , 1≤i≤n 3 .…”