Authors: Sungrae Park, Seung Shin, Seonghyeon Kim, Jaeheung Surh, Junyeop Lee, Hwalsuk Lee

Description: Our model consists of a ResNet-based backbone, a sequence model, and an attention-based decoder [1]. The backbone is a combination of the ResNet and SENet(squeeze and excitation network) [2] and the others are based on Baek et al. [1]. We trained the model with our own synthetic datasets by applying virtual adversarial training (VAT) techniques [3]. For this competition, we fine-tuned the model with the training dataset of SROIE. The recognition identified the texts on the detected text boxes by CRAFTS [4].

Authors: IFLYTEK

Description: Description: an attention-based text recognizer is designed as an encoder-decoder framework. In the encoding stage, an image is transformed into a sequence of feature vectors by CNN/LSTM, and each feature vector corresponds to a region in the input image. In the decoding stage, the attention model first computes alignment factors by referring to the history of target characters and the encoded feature vectors for generating the synthesis vectors. Then, a recurrent neural network (RNN) is used to generate the target characters based on the glimpse vectors and the history of target characters.

Authors: caisiqi

Description: 先CTPN检测，CRNN识别字块图得到不含空格的文本序列，后处理添加序列中单词间的空格。
先CTPN检测，CRNN识别字块图得到不含空格的文本序列，后处理添加序列中单词间的空格。
先CTPN检测，CRNN识别字块图得到不含空格的文本序列，后处理添加序列中单词间的空格。