Hybrid neural network architecture for age identification of ancient Kannada scripts

Abstract

Wide research has been canied out and is still taking place in the field of character, recognition of handwritten English characters. Recognizing English characters is much simpler as there are only 26 letters and each letter is quite distinct from others compared to recognition of Indian language characters. Indian language characters have a base character along with vowels attached, forming single characters (raw characters). Origin of Kannada, a language of southern India, is as old as 5Ih century AD, The fonts have evolved over the centuries. The work involved is implemented in two phases. The first phase of the work incorporates an Artificial Neural Network for identifying the base character. The second phase consists of a Probabilistic Neural Network model designed for the identification of age pertaining to the base character. Characters dated from 3"' century BC to the present day are used for analysis and experimental results.

Performance Analysis of Random Forests with SVM and KNN in Classification of Ancient Kannada Scripts

“…It claims that the advantage of the approach is that it can be easily adapted for the identification of other ancient document collections. [10] proposes a texture-based approach for text recognition in ancient documents. It copes with the challenges such as degradation, staining, fluctuating text lines, superimposition of text etc.…”

Intelligent Recognition of Ancient Persian Cuneiform Characters

“…Application of Artificial Neural Networks (ANN) for pattern recognition and character recognition has been more widely reported in literature recent times. This has led to high expectation of what neural networks can do for different fields, especially fields where other approaches have not been successful (Kashyap et al, 2003).…”

“…Many researchers have been working on scripts recognition for more than three decades. Nevertheless, it remains to be one of the most challenging problems in pattern recognition (Kashyap et al, 2003).…”