Abstract
Reading text from natural images is much more difficult than from scanned text documents since the text may appear in all colors, different sizes and types, often with distorted geometry or textures applied. The paper presents the idea of high-speed image preprocessing algorithms utilizing the quasi-local histogram based methods such as binarization, ROI filtering, line and corners detection, etc. which can be helpful for this task. Their low computational cost is provided by a reduction of the amount of processed information carried out by means of a simple random sampling. The approach presented in the paper allows to minimize some problems with the implementation of the OCR algorithms operating on natural images on devices with low computing power (e.g. mobile or embedded). Due to relatively small computational effort it is possible to test multiple hypotheses e.g. related to the possible location of the text in the image. Their verification can be based on the analysis of images in various color spaces. An additional advantage of the discussed algorithms is their construction allowing an efficient parallel implementation further reducing the computation time.
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Lech, P., Okarma, K. (2016). Methods of Natural Image Preprocessing Supporting the Automatic Text Recognition Using the OCR Algorithms. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_17
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DOI: https://doi.org/10.1007/978-3-319-23814-2_17
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