Multi-modular architecture based on convolutional neural networks for online handwritten character recognition

In this paper, several convolutional neural network architectures are investigated for online isolated handwritten character recognition (Latin alphabet). Two main architectures have been developed and optimised. The first one, a TDNN, processes online features extracted from the character. The second one, a SDNN, relies on the off-line bitmaps reconstructed from the trajectory of the pen. Moreover, an hybrid architecture called SDTDNN has been derived, it allows the combination of on-line and off-line recognisers. Such a combination seems to be very promising to enhance the character recognition rate. This type of shared weights neural networks introduces the notion of receptive field, local extraction and it allows to restrain the number of free parameters in opposition to classic techniques such as multi-layer perceptron. Results on UNIPEN and IRONOFF databases for online recognition are reported, while the MNIST database has been used for the off-line classifier.