Abstract
Natural scene or document images captured from camera devices containing text are the most informative region for communication. Extraction of text regions from such images is the primary and fundamental task of obtaining textual content present in images. Classifying foreground objects as text/non-text elements is one of the significant modules in scene text localization. Stroke width is an important discriminating feature of text blocks. In this paper, a distance transform-based stroke feature descriptor is reported for component level classification of foreground components obtained from input images. Potential stroke pixels are identified from distance map of a component using strict staircase method, and distribution of distance values of such pixels is used for designing the feature descriptors. Finally, we classify the components using a neural network-based classifier. Experimental result shows that component classification accuracy is more than 88%, which is much impressive in practical scenario.
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Acknowledgements
This work is carried out in the research lab of Computer Science & Engineering Department of Aliah University. The first author is grateful to Maulana Azad National Fellowship (MANF) for the financial support.
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Khan, T., Mollah, A.F. (2019). Distance Transform-Based Stroke Feature Descriptor for Text Non-text Classification. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_19
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DOI: https://doi.org/10.1007/978-981-13-1280-9_19
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