method: Task2-re52019-04-30
Authors: Yumei Li, Jianwei Wu, Wenhao He (angelicohe@tencent.com), Tao Xue, Long Liu
Description: We combine the results of CNN and RNN models. Firstly, we recognize characters by sliding the text line image with character models, which are learned in an end-to-end manner on text line images labeled with text transcripts. The character classifier outputs on the sliding windows are normalized and decoded with Connectionist Temporal Classification (CTC) based algorithm. Secondly, we use a neural network model — based on Convolutional Neural Networks, Recurrent Neural Networks and a novel attention mechanism to get the results. Finally, we do post-processing based on the dictionary, and vote for the final results. In addition to the training set provided by ReCTS 2019, we used the public dataset, including MLT,ICDAR,CASIA10K,COCO-Text, and synthetic data sets.
Organization: Tencent Map Big Data Lab Image Recognition Team
References
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