  Handwritten digit or numeral recognition is one of the classical issues in
the area of pattern recognition and has seen tremendous advancement because of
the recent wide availability of computing resources. Plentiful works have
already done on English, Arabic, Chinese, Japanese handwritten script. Some
work on Bangla also have been done but there is space for development. From
that angle, in this paper, an architecture has been implemented which achieved
the validation accuracy of 99.44% on BHAND dataset and outperforms Alexnet and
Inception V3 architecture. Beside digit recognition, digit generation is
another field which has recently caught the attention of the researchers though
not many works have been done in this field especially on Bangla. In this
paper, a Semi-Supervised Generative Adversarial Network or SGAN has been
applied to generate Bangla handwritten numerals and it successfully generated
Bangla digits.
