Pushing the Performance Limit of Scene Text Recognizer without Human Annotation
Author:
Affiliation:
1. School of Computer Science and Ningbo Institute, Northwestern Polytechnical University,China
2. Samsung R&D Institute China Xi'an (SRCX)
3. Samsung Advanced Institute of Technology (SAIT),South Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879089.pdf?arnumber=9879089
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1. An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition
2. Regularization with stochastic transformations and perturbations for deep semi-supervised learning;sajjadi;NIPS,2016
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4. Recognizing text with perspective distortion in natural scenes;quy phan;ICCV,2013
5. PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
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