Abstract
Text serves as an excellent persistent communication medium for unambiguous and precise information exchange. Text could help us to describe any scene. Hence, it would be ideal to understand scene text to accurately identify and understand a scene image or video. Variations such as script, font, color, scale, lighting, angle of view and other distortions make scene text understanding a challenge. Detecting and localizing the possible text, could improve the task of text understanding. Though decades of research had attempted to address the problem, still it is an open area. For instance, requirement of high-performance computation platform, large training dataset and longer training process. We have attempted to train our auto encoder based text detector to precisely localize text with minimum training on a small dataset and limited computational resources. The idea involves computation of morphological gradient to enhance text on the scene image and to feed it to a gradient auto encoder neural network to locate possible text components. The proposed detector can detect text across multiple languages and it is robust against the variations such as scale, orientation, font, and lighting. The results are promising. The proposed method achieves an F-measure of 0.75 and 0.76 on MRRC dataset and MSRA-TD500 dataset respectively, after training with 167 images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ajay, B.N., Naveena, C.: A mechanism for detection of text in images using DWT and MSER. In: Krishna, A.N., Srikantaiah, K.C., Naveena, C. (eds.) Integrated Intelligent Computing, Communication and Security. SCI, vol. 771, pp. 669–676. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-8797-4_68
Basavaraju, H., et al.: Neighborhood structure-based model for multilingual arbitrarily-oriented text localization in images/videos (2021)
Basavaraju, H.T., Manjunath Aradhya, V.N., Guru, D.S.: A novel arbitrary-oriented multilingual text detection in images/video. In: Satapathy, S.C., Joao Manuel, R.S., Tavares, V.B., Mohanty, J.R. (eds.) Information and decision sciences. AISC, vol. 701, pp. 519–529. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7563-6_54
Basavaraju, H.T., et al.: LoG and structural based arbitrary oriented multilingual text detection in images/video. Int. J. Nat. Comput. Res. (IJNCR). 7(3), 1–16 (2018)
Basu, S., et al.: Multilingual scene text detection using gradient morphology. Int. J. Comput. Vis. Image Process. 10(3), 31–43 (2020). https://doi.org/10.4018/IJCVIP.2020070103
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Chen, D., Luettin, J.: A survey of text detection and recognition in images and videos (2000)
Chen, H., et al.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 2011 18th IEEE International Conference on Image Processing, pp. 2609–2612 (2011). https://doi.org/10.1109/ICIP.2011.6116200
Chollet, F.: Others: Keras (2015)
Coates, A., et al.: Text detection and character recognition in scene images with unsupervised feature learning. In: 2011 International Conference on Document Analysis and Recognition, pp. 440–445 (2011). https://doi.org/10.1109/ICDAR.2011.95
Epshtein, B., et al.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970 (2010). https://doi.org/10.1109/CVPR.2010.5540041
Fu, K., et al.: Text detection for natural scene based on MobileNet V2 and U-Net. In: 2019 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1560–1564 (2019). https://doi.org/10.1109/ICMA.2019.8816384
He, T., et al.: Text-attentional convolutional neural network for scene text detection. IEEE Trans. Image Process. 25(6), 2529–2541 (2016). https://doi.org/10.1109/TIP.2016.2547588
Jung, K., et al.: Text information extraction in images and video: a survey. Pattern Recogn. 37(5), 977–997 (2004). https://doi.org/10.1016/j.patcog.2003.10.012
Kumar, D., et al.: Multi-script robust reading competition in ICDAR 2013. In: Proceedings of the 4th International Workshop on Multilingual OCR. Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2505377.2505390
Li, H., Lu, H.: AT-Text: assembling text components for efficient dense scene text detection. Future Internet. 12(11), 1–14 (2020). https://doi.org/10.3390/fi12110200
Liao, M., et al.: Rotation-sensitive regression for oriented scene text detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5909–5918 (2018). https://doi.org/10.1109/CVPR.2018.00619
Liu, Y., et al.: Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recogn. 90, 337–345 (2019). https://doi.org/10.1016/j.patcog.2019.02.002
Long, S., He, X., Yao, C.: Scene text detection and recognition: the deep learning era. Int. J. Comput. Vision 129(1), 161–184 (2020). https://doi.org/10.1007/s11263-020-01369-0
Manjunath Aradhya, V.N., Basavaraju, H.T., Guru, D.S.: Decade research on text detection in images/videos: a review. Evol. Intel. 14(2), 405–431 (2019). https://doi.org/10.1007/s12065-019-00248-z
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/ (2015)
Matas, J., et al.: Robust wide-baseline stereo from maximally stable extremal regions. In: Image and Vision Computing (2004). https://doi.org/10.1016/j.imavis.2004.02.006
Rivest, J.-F., et al.: Morphological gradients. J. Electron. Imaging 2(4), 326–336 (1993). https://doi.org/10.1117/12.159642
Shekar, B.H., et al.: Discrete wavelet transform and gradient difference based approach for text localization in videos. In: Proceedings - 2014 5th International Conference on Signal and Image Processing, ICSIP 2014, pp. 280–284 (2014). https://doi.org/10.1109/ICSIP.2014.50
Shekar, B.H., Raveeshwara, S.: Contour feature learning for locating text in natural scene images. Int. J. Inf. Technol. 14, 1–6 (2022). https://doi.org/10.1007/s41870-021-00851-3
Shekar, B.H., Raveeshwara, S.: Morphological gradient analysis and contour feature learning for locating text in natural scene images. In: International Conference on Computer Vision and Image Processing, pp. 254–261 (2022)
Shekar, B.H., Smitha M., L.: Morphological gradient based approach for text localization in video/scene images. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2426–2431 (2014). https://doi.org/10.1109/ICACCI.2014.6968426
Wan, Z., et al.: TextScanner: reading characters in order for robust scene text recognition. arXiv (2019). https://doi.org/10.1609/aaai.v34i07.6891
Wang, X., et al.: Arbitrary shape scene text detection with adaptive text region representation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 6442–6451 (2019). https://doi.org/10.1109/CVPR.2019.00661
Wu, V., et al.: Textfinder: an automatic system to detect and recognize text in images. IEEE Trans. Pattern Anal. Mach. Intell. 21(11), 1224–1229 (1999). https://doi.org/10.1109/34.809116
Yang, Q., et al.: Inceptext: a new inception-text module with deformable PSROI pooling for multi-oriented scene text detection. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 1071–1077 (2018). https://doi.org/10.24963/ijcai.2018/149
Yao, C., et al.: Detecting texts of arbitrary orientations in natural images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 8, pp. 1083–1090 (2012). https://doi.org/10.1109/CVPR.2012.6247787
Yao, C., et al.: Scene text detection via holistic, multi-channel prediction, pp. 1–10 (2016)
Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1480–1500 (2014)
Yin, X.C., et al.: Text detection, tracking and recognition in video: a comprehensive survey. IEEE Trans. Image Process. 25(6), 2752–2773 (2016). https://doi.org/10.1109/TIP.2016.2554321
Zhang, Y., Huang, Y., Zhao, D., Wu, C.H., Ip, W.H., Yung, K.L.: A scene text detector based on deep feature merging. Multimedia Tools Appl. 80(19), 29005–29016 (2021). https://doi.org/10.1007/s11042-021-11101-w
Zhang, Z., et al.: Multi-oriented text detection with fully convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4159–4167 (2016). https://doi.org/10.1109/CVPR.2016.451
Zhong, Y., et al.: Locating text in complex color images. Pattern Recogn. 28(10), 1523–1535 (1995). https://doi.org/10.1016/0031-3203(95)00030-4
Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2642–2651 (2017). https://doi.org/10.1109/CVPR.2017.283
Zhu, A.: Scene text detection and recognition. Front. Comp. Sci. 10(1), 19–36 (2017)
Zhu, Y., Yao, C., Bai, X.: Scene text detection and recognition: recent advances and future trends. Front. Comp. Sci. 10(1), 19–36 (2016). https://doi.org/10.1007/s11704-015-4488-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Raveeshwara, S., Shekar, B.H. (2023). Scene Text Detection with Gradient Auto Encoders. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)