Identification of typewritten and handwritten Conjunct Gujarati characters using artificial neural network

Gujarati script has a large number of characters with curvature shapes and complexities. The script can contain a variety of character sets like vowels, consonants, numerals, modifiers, conjunct characters, and other combinations of characters. Different forms of conjunct characters are possible but in this particular paper one of the forms of frequently used conjunct characters is considered for assessment point of view. This paper deals with the identification of typewritten and handwritten conjunct characters. There is no benchmark dataset available for Conjunct Gujarati characters; so, a train and test datasets are created using various features of characters such as a number of open edges, location of open edges in zone, pixels count on a constructed horizontal and vertical line. Artificial neural network is used for the classification of characters and a success rate of 99.4% and 94.1% is achieved in most of the typewritten and handwritten Gujarati conjunct characters respectively.