  The work presented here involves the design of a Multi Layer Perceptron (MLP)
based classifier for recognition of handwritten Bangla alphabet using a 76
element feature set Bangla is the second most popular script and language in
the Indian subcontinent and the fifth most popular language in the world. The
feature set developed for representing handwritten characters of Bangla
alphabet includes 24 shadow features, 16 centroid features and 36 longest-run
features. Recognition performances of the MLP designed to work with this
feature set are experimentally observed as 86.46% and 75.05% on the samples of
the training and the test sets respectively. The work has useful application in
the development of a complete OCR system for handwritten Bangla text.
