Offline Arabic Handwriting Recognition Using BLSTMs Combination

We propose in this paper, an Arabic handwriting recognition system based on multiple BLSTM-CTC combination architectures. Given several feature sets, the low-level fusion consisted in projecting them into a unique feature space. Mid-level combination methods were performed using two techniques: the first one consists in averaging the a-posteriori probabilities of each individual BLSTM, and injecting them in the CTC decoding. The second is based on the training of a new BLSTM-CTC system using the sum of the a-posteriori probabilities generated by the individual systems. The high-level fusion is based on the combination of the individual decoding outputs. Lattice combination and ROVER strategies were evaluated in this context. The experiments conducted on the KHATT database showed that the high-level combination method significantly improves the recognition rate compared to the other fusion strategies.