Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/215533
Title: Deep neural network with attention model for scene text recognition
Authors: Shuohao Li;Min Tang;Qiang Guo;Jun Lei;Jun Zhang
Year: 2017
Publisher: IET
Abstract: The authors present a deep neural network (DNN) with attention model for scene text recognition. The proposed model does not require any segmentation of the input text image. The framework is inspired by the attention model presented recently for speech recognition and image captioning. In the proposed framework, feature extraction, feature attention and sequence recognition are integrated in a jointly trainable network. Compared with previous approaches, the following contributions are mainly made. (i) The attention model is applied into DNN to recognise scene text, and it can effectively solve the sequence recognition problem caused by variable length labels. (ii) Rigorous experiments are performed across a number of challenging benchmarks, including IIIT5K, SVT, ICDAR2003 and ICDAR2013 datasets. Results in experiments show that the proposed model is comparable or better than the state-of-the-art methods. (iii) This model only contains 6.5 million parameters. Compared with other DNN models for scene text recognition, this model has the least number of parameters so far.
URI: http://localhost/handle/Hannan/215533
volume: 11
issue: 7
More Information: 605,
612
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8039270.pdf2.82 MBAdobe PDF
Title: Deep neural network with attention model for scene text recognition
Authors: Shuohao Li;Min Tang;Qiang Guo;Jun Lei;Jun Zhang
Year: 2017
Publisher: IET
Abstract: The authors present a deep neural network (DNN) with attention model for scene text recognition. The proposed model does not require any segmentation of the input text image. The framework is inspired by the attention model presented recently for speech recognition and image captioning. In the proposed framework, feature extraction, feature attention and sequence recognition are integrated in a jointly trainable network. Compared with previous approaches, the following contributions are mainly made. (i) The attention model is applied into DNN to recognise scene text, and it can effectively solve the sequence recognition problem caused by variable length labels. (ii) Rigorous experiments are performed across a number of challenging benchmarks, including IIIT5K, SVT, ICDAR2003 and ICDAR2013 datasets. Results in experiments show that the proposed model is comparable or better than the state-of-the-art methods. (iii) This model only contains 6.5 million parameters. Compared with other DNN models for scene text recognition, this model has the least number of parameters so far.
URI: http://localhost/handle/Hannan/215533
volume: 11
issue: 7
More Information: 605,
612
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8039270.pdf2.82 MBAdobe PDF
Title: Deep neural network with attention model for scene text recognition
Authors: Shuohao Li;Min Tang;Qiang Guo;Jun Lei;Jun Zhang
Year: 2017
Publisher: IET
Abstract: The authors present a deep neural network (DNN) with attention model for scene text recognition. The proposed model does not require any segmentation of the input text image. The framework is inspired by the attention model presented recently for speech recognition and image captioning. In the proposed framework, feature extraction, feature attention and sequence recognition are integrated in a jointly trainable network. Compared with previous approaches, the following contributions are mainly made. (i) The attention model is applied into DNN to recognise scene text, and it can effectively solve the sequence recognition problem caused by variable length labels. (ii) Rigorous experiments are performed across a number of challenging benchmarks, including IIIT5K, SVT, ICDAR2003 and ICDAR2013 datasets. Results in experiments show that the proposed model is comparable or better than the state-of-the-art methods. (iii) This model only contains 6.5 million parameters. Compared with other DNN models for scene text recognition, this model has the least number of parameters so far.
URI: http://localhost/handle/Hannan/215533
volume: 11
issue: 7
More Information: 605,
612
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8039270.pdf2.82 MBAdobe PDF