Abstract
Text detection from scenic photographs with text is a difficult issue that has recently attracted a lot of attention. There are two main elements in scenery photographs (1) Recognizing text in photographs and (2) Character recognition. The model’s entire accuracy depends on the output of this phase, finding the text in the photos is the most crucial aspect. An approach consisting of two phases has been proposed in this article. (1) Text recognition and (2) Text checker. Text detection is accomplished using the Maximally Stable Extremal Regions (MSER) feature detector. The output of the MSER feature detector is subjected to various filters in order to exclude components, i.e., unlikely to contain text. The second phase uses a machine learning methodology to classify the text and non-text on phase-1 final output. It has been discovered that the proposed method nearly removes all false-positive results on the MSER method’s final output.
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Yadav, A.K., Sharma, A., Yadav, V. et al. Rfpssih: reducing false positive text detection sequels in scenery images using hybrid technique. Int J Syst Assur Eng Manag 14, 2289–2300 (2023). https://doi.org/10.1007/s13198-023-02070-4
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DOI: https://doi.org/10.1007/s13198-023-02070-4