  Handwritten character recognition is a challenging research in the field of
document image analysis over many decades due to numerous reasons such as large
writing styles variation, inherent noise in data, expansive applications it
offers, non-availability of benchmark databases etc. There has been
considerable work reported in literature about creation of the database for
several Indic scripts but the Tamil script is still in its infancy as it has
been reported only in one database [5]. In this paper, we present the work done
in the creation of an exhaustive and large unconstrained Tamil Handwritten
Character Database (uTHCD). Database consists of around 91000 samples with
nearly 600 samples in each of 156 classes. The database is a unified collection
of both online and offline samples. Offline samples were collected by asking
volunteers to write samples on a form inside a specified grid. For online
samples, we made the volunteers write in a similar grid using a digital writing
pad. The samples collected encompass a vast variety of writing styles, inherent
distortions arising from offline scanning process viz stroke discontinuity,
variable thickness of stroke, distortion etc. Algorithms which are resilient to
such data can be practically deployed for real time applications. The samples
were generated from around 650 native Tamil volunteers including school going
kids, homemakers, university students and faculty. The isolated character
database will be made publicly available as raw images and Hierarchical Data
File (HDF) compressed file. With this database, we expect to set a new
benchmark in Tamil handwritten character recognition and serve as a launchpad
for many avenues in document image analysis domain. Paper also presents an
ideal experimental set-up using the database on convolutional neural networks
(CNN) with a baseline accuracy of 88% on test data.
