Abstract
The multi-font and multi-lingual handwritten numerals recognition has been a demanding requirement in this decade. This research work proposes multi-lingual handwritten numerals recognition using partial derivatives for classifying handwritten numerals of five major Indian languages. The objective of the proposed work aims at designing and developing a recognition algorithm for multilingual handwritten numerals. This objective is achieved through data collection and preprocessing which involves creation of handwritten numeral databases, data collection, round off mean aspect ratio value based representation and identification of features using partial derivatives. The features derived from partial derivatives are stored in a five dimensional column vector which yielded a recognition rate of 94.80, 95.89, 96.44, 95.81 and 92.03%, respectively for Kannada, Gurumukhi, Sindhi, Malayalam and Tamil Handwritten Numerals respectively.
Highlights
Extraction of numerals from student answer scripts, identification of distances from traffic information board, recognition of numerals information from tabular forms are some of the applications of a numeral recognition system
Recent computer system and communication technologies such as software packages like word processors with multiple fonts and multi size fonts, sending and receiving electronic mail and sending messages through fax machine have the impact of increasing the number of readers, literacy and the way of writing by the human beings
Mamatha et al (2011) have used k-means clustering algorithm for classification and directional chain code method to extract the features from the resized image of size 30-by-30 pixels and obtained 96% recognition rate for Kannada numerals
Summary
Extraction of numerals from student answer scripts, identification of distances from traffic information board, recognition of numerals information from tabular forms are some of the applications of a numeral recognition system. Kartar et al (2011) have used three different feature sets, namely, projection histogram, distance profile and Background Directional Distribution (BDD) resulting in the recognition rate of 99.2, 98 and 99.13%, respectively has been reported respectively, using SVM with Radial Basis Function (RBF) kernel classifier for Gurumukhi handwritten numerals, using only 150 samples. Mamatha et al (2011) have used k-means clustering algorithm for classification and directional chain code method to extract the features from the resized image of size 30-by-30 pixels and obtained 96% recognition rate for Kannada numerals. The objective of the proposed research work aims at, Development of handwritten numerals databases of five different Indian languages, namely, Kannada, Gurumukhi, Sindhi, Tamil and Malayalam, Finding round off mean aspect ratio value based representation scheme, Identification and extraction of features using partial derivatives and Recognition of multi lingual numerals using a distance metric
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