Focusing Attention: Towards Accurate Text Recognition in Natural Images
Zhanzhan Cheng,Fan Bai,Yunlu Xu,Gang Zheng,Shiliang Pu,Shuigeng Zhou +5 more
- pp 5086-5094
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TLDR
Zhang et al. as mentioned in this paper proposed Focusing Attention Network (FAN) which employs a focusing attention mechanism to automatically draw back the drifted attention. But the FAN method is not suitable for complex and low-quality images and it cannot get accurate alignment between feature areas and targets for such images.Abstract:
Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon “attention drift”. To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.read more
Citations
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Journal ArticleDOI
EPAN: Effective parts attention network for scene text recognition
TL;DR: The effective parts attention network (EPAN) is proposed which can attentively highlight the character region for more precise recognition and significantly outperformed or was comparable to existing methods in terms of lexicon-free word accuracy.
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SCUT-EPT: New Dataset and Benchmark for Offline Chinese Text Recognition in Examination Paper
TL;DR: It is observed that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR).
Proceedings ArticleDOI
Towards End-to-End Unified Scene Text Detection and Layout Analysis
Shangbang Long,Si Yuan Qin,Dmitry Panteleev,Alessandro Bissacco,Yasuhisa Fujii,Michalis Xenos and Anastasios Raptis +5 more
TL;DR: A novel method is proposed that is able to simultaneously detect scene text and form text clusters in a unified way and achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing.
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AutoSTR: Efficient Backbone Search for Scene Text Recognition.
TL;DR: In this article, the authors proposed an automated scene text recognition (AutoSTR) method to search data-dependent backbones to boost text recognition performance, which can outperform the state-of-the-art approaches on standard benchmarks.
Proceedings ArticleDOI
Handwritten Digit String Recognition using Convolutional Neural Network
Hongjian Zhan,Shujing Lyu,Yue Lu +2 more
TL;DR: This paper proposes a new architecture which is based on CNN only, and applies it to handwritten digit string recognition (HDSR), composed of three parts from bottom to top: feature extraction layers, feature dimension transposition layers and an output layer.
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