Shallow Neural Network Model for Hand-Drawn Symbol Recognition in Multi-Writer Scenario
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration.
Dey, Sounak; Dutta, Anjan; Llados, Josep; Fornes, Alicia; and Pal, Umapada, "Shallow Neural Network Model for Hand-Drawn Symbol Recognition in Multi-Writer Scenario" (2018). Conference Articles. 111.