A Robust Approach for Scene Text Localization Using Rule-Based Confidence Map and Grouping

This study presents a robust algorithm to localize both Farsi/Arabic and Latin scene texts with different sizes, fonts and orientations even the low luminance contrast and poor quality ones. First, a new region detector is proposed to extract the candidate text regions. It is an integration of the weighted median filtering, contrast preserving decolorization and MSER techniques. It is robust to the low luminance contrast and poor quality scene texts. Afterwards, a novel method based on the fuzzy inference systems (FIS) is proposed to build a confidence map. This map indicates the likelihood of being text for the extracted candidate regions. Therefore it is exploited to filter the nontext candidates. Finally, a new fuzzy-based approach is proposed to create the single arbitrarily oriented text lines. It is based on the clustering, FIS, minimum area rectangle as well as radon transform techniques. It could also retrieve some of the discarded isolated characters or subwords in the filtering stage. To validate the proposed algorithm, we created a collection of natural images containing both Farsi/Arabic and Latin texts. Compared with the state-of-the-art methods, the proposed method achieves the best performance on our and Epshtein datasets and competitive performances on the ICDAR dataset.