Name
Abstract | ||
|---|---|---|
Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/DAS.2012.6 | Document Analysis Systems |
Keywords | Field | DocType |
new method,dominant text pixel selection,arbitrarily-oriented text detection,neighboring text component,dominant text pixel,arbitrarily-oriented text,missing text information,text detection,text representative,video frame,candidate text representative,feature extraction,computer vision,image resolution,f measure,classification algorithms,pattern recognition,information extraction,region growing,edge detection | Computer vision,Pattern recognition,Computer science,Edge detection,Search engine indexing,Feature extraction,Sobel operator,Information extraction,Pixel,Region growing,Artificial intelligence,Statistical classification | Conference |
Citations | PageRank | References |
27 | 0.93 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nabin Sharma | 1 | 132 | 11.55 |
| Palaiahnakote Shivakumara | 2 | 774 | 64.90 |
| Umapada Pal | 3 | 1477 | 139.32 |
| Michael Blumenstein | 4 | 47 | 4.20 |
| Chew Lim Tan | 5 | 4484 | 284.26 |