  We describe a method for classification of handwritten Kannada characters
using Hidden Markov Models (HMMs). Kannada script is agglutinative, where
simple shapes are concatenated horizontally to form a character. This results
in a large number of characters making the task of classification difficult.
Character segmentation plays a significant role in reducing the number of
classes. Explicit segmentation techniques suffer when overlapping shapes are
present, which is common in the case of handwritten text. We use HMMs to take
advantage of the agglutinative nature of Kannada script, which allows us to
perform implicit segmentation of characters along with recognition. All the
experiments are performed on the Chars74k dataset that consists of 657
handwritten characters collected across multiple users. Gradient-based features
are extracted from individual characters and are used to train character HMMs.
The use of implicit segmentation technique at the character level resulted in
an improvement of around 10%. This system also outperformed an existing system
tested on the same dataset by around 16%. Analysis based on learning curves
showed that increasing the training data could result in better accuracy.
Accordingly, we collected additional data and obtained an improvement of 4%
with 6 additional samples.
