Variational Connectionist Temporal Classification

Linlin Chao, Jingdong Chen, Wei Chu ;

Connectionist Temporal Classification (CTC) is a training criterion designed for sequence labelling problems where the alignment between the inputs and the target labels is unknown. One of the key steps is to add a blank symbol to the target vocabulary. However, CTC tends to output spiky distributions since it prefers to output blank symbol most of the time. These spiky distributions show inferior alignments and the non-blank symbols are not learned sufficiently. To remedy this, we propose variational CTC (Var-CTC) to enhance the learning of non-blank symbols. The proposed Var-CTC converts the output distribution of vanilla CTC with hierarchy distribution. It first learns the approximated posterior distribution of blank to determine whether to output a specific non-blank symbol or not. Then it learns the alignment between non-blank symbols and input sequence. Experiments on scene text recognition and offline handwritten text recognition show Var-CTC achieves better alignments. Besides, with the enhanced learning of non-blank symbols, the confidence scores of model outputs are more discriminative. Compared with the vanilla CTC, the proposed Var-CTC can improve the recall performance by a large margin when the models maintain the same level of precision."