Handwritten Digit Recognition using DAISY Descriptor: A Study

Handwritten digit recognition is a highly evolved research domain. The major issues that make this domain challenging are different photometric discrepancies, along with computation complexity. A script invariant feature vector is designed here based on the concept of the DAISY descriptor which has previously been applied in different research domains. We have applied this feature descriptor after suitable customization to fit it into the aforesaid classification problem. We have tested the same on handwritten digits written in four different scripts namely Arabic, Bangla, Devanagari and Roman. Bangla dataset is in-house, while the remaining are the standard databases. Experimental results demonstrate the effectiveness of the said feature descriptor for digit recognition. It is a computationally inexpensive approach when compared to other state-of-the-art prevalent architectures like LSTM or CNN.