TY - JOUR KW - Multilingual Text KW - Clustering KW - Computer vision KW - Image Processing KW - Maxmin Cluster KW - Arbitrarily-Oriented AU - H.T. Basavaraju AU - V.N. Manjunath Aradhya AU - D.S. Guru AB - The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation. Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground. The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information. In this work, an effective model is presented to locate the multilingual arbitrary oriented text. The proposed method developed a neighborhood structure model to locate the text region. Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region. The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image. Finally, the double stroke structure model is employed to locate the accurate text region. The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure. IS - Regular Issue M1 - 2 N2 - The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation. Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground. The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information. In this work, an effective model is presented to locate the multilingual arbitrary oriented text. The proposed method developed a neighborhood structure model to locate the text region. Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region. The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image. Finally, the double stroke structure model is employed to locate the accurate text region. The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure. PY - 2021 SP - 134 EP - 140 T2 - International Journal of Interactive Multimedia and Artificial Intelligence TI - Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos UR - https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_12_0.pdf VL - 7 SN - 1989-1660 ER -