Abstract
In the proposed paper, we consider a problem of text detection in document images. This problem plays an important role in OCR systems and is a challenging task. In the first step of our proposed text detection approach, we use a self-adjusting bottom-up segmentation algorithm to segment a document image into a set of connected components (CCs). The segmentation algorithm is based on the Sobel edge detection method. In the second step, CCs are described in terms of 27 features and a machine learning algorithm is then used to classify the CCs as text or nontext. For testing the approach, we have collected a dataset (ASTRoID), which contains 500 images of text blocks and 500 images of nontext blocks. We empirically compare performance of the proposed text detection method when using seven different machine learning algorithms.
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Acknowledgments
The presented work was supported by Creative Core FISNM-3330-13-500033 ‘Simulations’ project funded by the European Union, The European Regional Development Fund. The operation is carried out within the framework of the Operational Programme for Strengthening Regional Development Potentials for the period 2007–2013, Development Priority 1: Competitiveness and research excellence, Priority Guideline 1.1: Improving the competitive skills and research excellence.
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Zelenika, D., Povh, J., Ženko, B. (2016). Text Detection in Document Images by Machine Learning Algorithms. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_16
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DOI: https://doi.org/10.1007/978-3-319-26227-7_16
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