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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Book ChapterDOI
Character Flow Detection and Rectification for Scene Text Spotting.
TL;DR: Zhang et al. as mentioned in this paper proposed a three-stage bottom-up scene text spotter, including text segmentation, text rectification and text recognition, which adopts a feature pyramid network (FPN) to extract character instances by combining local and global information, then a joint network of FPN and bidirectional LSTM is developed to explore the affinity among the isolated characters, which are grouped into character flows.
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