Handwriting Recognition with Large Multidimensional Long Short Term Memory Recurrent Neural Networks
Multidimensional long short term memory recurrent neural networks achieve impressive results for handwriting recognition. However, with current CPU based implementations, their training is very expensive and thus their capacity has so far been limited. We release an efficient GPU based implementation which greatly reduces training times by processing the input in a diagonal wise fashion. We use this implementation to explore deeper and wider architectures than previously used for handwriting recognition and show that especially the depth plays an important role. We outperform state of the art results on two databases with a deep multidimensional network.
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)