Abstract
A novel method is proposed for text extraction from mail images with complex background. Firstly, wavelet transform and Laplacian operator are applied to generate the features of regions which are obtained by dividing input image with sliding window. Then, support vector machine (SVM) is utilized to classify these regions into texts and non-texts according to the features. Bootstrap strategy is used to build the training database. Finally, connected components analysis (CCA) is employed to merge text regions into text candidates which can be processed by following steps to get the delivery address. Experimental results involving 534 mail images show the effectiveness and robustness of the proposed method, and comparison results with other methods demonstrate the advantages of the selected features.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
He, P., Huang, W., He, T., Zhu, Q., Qiao, Y., Li, X.: Single shot textdetector with regional attention. In: International Conference on Computer Vision (2017)
He, T., Huang, W., Qiao, Y., Yao, J.: Accurate text localization in natural image with cascaded convolutional text network. arXiv:1603.09423 (2016)
He, T., Huang, W., Qiao, Y., Yao, J.: Text-attention convolutional neural networks for scene text detection. IEEE Trans. Image Process. 25, 2529–2541 (2016)
Iqbal, K., Yin, X., Yin, X., Ali, H., Hao, H.: Classifier comparison for MSER-based text classification in scene images. In: International Joint Conference on Neural Networks, pp. 1–6 (2013)
Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., Luo, Z.: R2CNN: Rotation region CNN for orientation robust scene text detection. arXiv:1706.09579v2 (2017)
Koo, K., Kim, D.: Scene text detection via connected component clustering and nontext filtering. IEEE Trans. Image Process. 22(6), 2296–2305 (2013)
Liao, M., Shi, B., Bai, X, Wang, X., Liu, W.: Textboxes: a fast textdetector with a single deep neural network. In: The 31th AAAI Conference on Artificial Intelligence, pp. 4161–4167 (2017)
Lienhart, R., Wernicke, A.: Localizing and segmenting text in images and videos. IEEE Trans. Circuits Syst. Video Technol. 12(4), 256–268 (2002)
Liu, C., Wang, C., Dai, R.: Text detection in images based on unsupervised classification of edge-based features. In: The 18th International Conference on Document Analysis and Recognition, pp. 610–614 (2005)
Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3538–3545 (2012)
Pan, Y.F., Hou, X., Liu, C.L.: A hybrid approach to detect and localize texts in natural scene images. IEEE Trans. Image Process. 20(3), 800–813 (2011)
Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S.: Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recogn. Lett. 34(2), 107–116 (2013)
Shivakumara, P., Trung, Q.P., Tan, C.L.: A robust wavelet transform based technique for video text detection. In: The 10th International Conference on Document Analysis and Recognition, pp. 1285–1289 (2009)
Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)
Tu, X., Lu, Y.: Run-based approach to labeling connected components in document images. In: The 2th International Workshop on ETCS, pp. 206–209 (2010)
Ye, Q., Gao, W., Wang, W., Zeng, W.: A robust text detection algorithm in images and video frames. In: IEEE ICICS-PCM, pp. 802–806 (2003)
Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Trans. Image Process. 20(9), 2594–2605 (2011)
Yin, X., Yin, X., Huang, K., Hao, H.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (2014)
Zhang, J., Kasturi, R.: Text detection using edge gradient and graph spectrum. In: The 20th International Conference on Pattern Recognition, pp. 3979–3982 (2010)
Zini, L., Destrero, A., Odone, F.: A classification architecture based on connected components for text detection in unconstrained environments. In: The 6th IEEE International Conference on Digital Object Identifier, pp. 176–181 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Q., Tu, X., Lu, S., Lu, Y. (2018). Text Extraction from Mail Images with Complex Background. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-8108-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8107-1
Online ISBN: 978-981-10-8108-8
eBook Packages: Computer ScienceComputer Science (R0)