Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images

Shivakumara, Palaiahnakote and Roy, Sangheeta and Jalab, Hamid Abdullah and Ibrahim, Rabha Waell and Pal, Umapada and Lu, Tong and Khare, Vijeta and Wahab, Ainuddin Wahid Abdul (2019) Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images. Expert Systems with Applications, 118. pp. 1-19. ISSN 0957-4174, DOI

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Retrieving desired information from databases containing video, natural scene, and license plate images through keyword spotting is a big challenge to expert systems due to different complexities that occur because of background and foreground variations of texts in real-time environments. To reduce background complexity of input images, we introduce a new model based on fractional means that considers neighboring information of pixels to widen the gap between text and background. To do so, the process obtains text candidates with the help of k-means clustering. The proposed approach explores the combination of Radon and Fourier coefficients to define context features based on regular patterns given by coefficient distributions for foreground and background of text candidates. This process eliminates non-text candidates regardless of different font types and sizes, colors, orientations and scripts, and results in representatives of texts. The proposed approach then exploits the fact that text pixels share almost the same values to restore missing text components using Canny edge image by proposing a new idea of minimum cost path based ring growing, and then outputs keywords. Furthermore, the proposed approach extracts the same above-mentioned features locally and globally for spotting words from images. Experimental results on different benchmark databases, namely, ICDAR 2013, ICDAR 2015, YVT, NUS video data, ICDAR 2013, ICDAR 2015, SVT, MSRA, UCSC, Medialab and Uninusubria license plate data show that the proposed method is effective and useful compared to the existing methods.

Item Type: Article
Funders: University of Malaya under Grant No: UM.0000520/HRU.BK (BKS003-2018), National Natural Science Foundation of China under Grant No. 61672273, No. 61272218 and No. 61832008, Natural Science Foundation for Distinguished Young Scholars of Jiangsu under Grant No. BK20160021, Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines)
Uncontrolled Keywords: Text candidates; Fractional mean; Radon coefficient; Fourier coefficient; Context features; Minimum cost path estimation; Ring growing; Word detection; Keyword spotting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 15 Jan 2019 00:54
Last Modified: 15 Jan 2019 00:54

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