DocumentCode :
2148585
Title :
Video Script Identification Based on Text Lines
Author :
Phan, Trung Quy ; Shivakumara, Palaiahnakote ; Ding, Zhang ; Lu, Shijian ; Tan, Chew Lim
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2011
fDate :
18-21 Sept. 2011
Firstpage :
1240
Lastpage :
1244
Abstract :
In this paper, we present a new method for video script identification which is essential before choosing an appropriate OCR engine for identifying text lines when a video frame contains more than one language. The input for script identification is the text lines obtained by our text detection method. We extract upper and lower extreme points for each connected component of Canny edges of text lines. The extracted points are connected to study the behavior of upper and lower lines. The direction of each 10-pixel segment of the lines is determined using PCA. The average angle of the segments of the upper and lower lines is computed to study the smoothness and cursiveness of the lines. In addition, to discriminate the scripts accurately, the method divides a text line into five equal zones horizontally to study the smoothness and cursiveness of the upper and lower lines of each zone. We evaluate the method by conducting experiments on different combinations of languages such as English and Chinese, English and Tamil, Chinese and Tamil, and English, Chinese and Tamil.
Keywords :
document image processing; natural language processing; object detection; principal component analysis; text analysis; video signal processing; Canny edges; OCR engine; PCA; text detection method; text line identification; video script identification; Feature extraction; Image edge detection; Optical character recognition software; Testing; Text recognition; Cursiveness; Smoothness; Upper and lower points; Video scrpt line identification; Video text line;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2011 International Conference on
Conference_Location :
Beijing
ISSN :
1520-5363
Print_ISBN :
978-1-4577-1350-7
Electronic_ISBN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2011.250
Filename :
6065508
Link To Document :
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