Word Spotting in Scene Images Based on Character Recognition

In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.

[1]  Ernest Valveny,et al.  Word Spotting and Recognition with Embedded Attributes , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Dimosthenis Karatzas,et al.  TextProposals: A text-specific selective search algorithm for word spotting in the wild , 2016, Pattern Recognit..

[3]  Alberto Del Bimbo,et al.  Reading Text in the Wild from Compressed Images , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[4]  Andrew D. Bagdanov,et al.  FAST: Facilitated and Accurate Scene Text Proposals through FCN Guided Pruning , 2017, Pattern Recognit. Lett..

[5]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[6]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jon Almazán,et al.  ICDAR 2013 Robust Reading Competition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[8]  Xiaolin Li,et al.  Single Shot Text Detector with Regional Attention , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Andrew D. Bagdanov,et al.  Improving Text Proposals for Scene Images with Fully Convolutional Networks , 2017, ArXiv.

[10]  Ankush Gupta,et al.  Synthetic Data for Text Localisation in Natural Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Chunhua Shen,et al.  Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  Andrew Zisserman,et al.  Deep Features for Text Spotting , 2014, ECCV.

[13]  Wenyu Liu,et al.  TextBoxes: A Fast Text Detector with a Single Deep Neural Network , 2016, AAAI.