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- W4313006883 abstract "The characteristics of bill-type text include multiple single characters, low semantic features and multiple text regions. For bill-type text recognition, the existing recognition methods have difficulty in model fitting and slow inference speed due to the deep CNN or RNN in the network. For the above issues, a high-precision bill-type text recognition model BT-CCN, which does not need to split characters and has fast inference speed, is proposed. BT-CCN can be divided into three parts: convolution module, sequence module and fully con-nected layer module. Firstly, the convolutional module extracts the image as feature sequence using the im-proved lightweight convolutional and asymmetric pooling structure. Secondly, BiTCN structure is proposed in sequence module, which can realize deep convolution and extract semantic features of sequence. Finally, a full connection layer is introduced to capture global information to avoid the constraint of the receptive field of the sequence module. Experimental results show that the model has higher precision and faster inference speed than other models on data sets of various practical projects." @default.
- W4313006883 created "2023-01-05" @default.
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- W4313006883 date "2022-09-23" @default.
- W4313006883 modified "2023-09-27" @default.
- W4313006883 title "Bill-Type Text Recognition Model Based on Convolution Network" @default.
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- W4313006883 doi "https://doi.org/10.1145/3565291.3565327" @default.
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