DocumentCode :
2648489
Title :
Fast text line detection by finding linear connected components on Canny edge image
Author :
Jung Il Hyun ; Hae Kwang Kim ; Weon Gun Oh
Author_Institution :
Dept. of Comput. Eng., Sejong Univ., Seoul, South Korea
fYear :
2015
fDate :
28-30 Jan. 2015
Firstpage :
1
Lastpage :
4
Abstract :
This paper presents a new way of text region detection on the basis of Canny edge detection and connected component. A Canny edge image is detected from a gray image obtained from an original color image. The Canny image is partitioned into n × n blocks and each n × n block is divided into smaller m × m blocks. If there are sufficient edge pixels in the m × m block, then the block is set to text candidate block. The number of text candidate blocks is counted in each n × n block, and the number is sufficient, then the n × n block is set to candidate text n × n block. Text regions are only detected in the candidate text n × n blocks. Connected-component is obtained from the edge pixels in the candidate text n × n blocks of the Canny edge image. The connected components are sorted with its size and grouped in to several groups. From each group, possible candidate text lines of connected components are detected and the connected components in the neighboring groups are added into the candidate text lines. The performance of proposed method is compared with the SWT (Stroke Width Transform) and Tesseraci text region detection method. The experimental results show that proposed one is faster than SWT losing accuracy and is slower than Tesseraci with better precision.
Keywords :
edge detection; object detection; transforms; Canny edge image detection; SWT; Tesseraci text region detection method; fast text line detection; gray image; image partitoning; linear connected components; stroke width transform; text region detection; Canny edge; connected component; text region detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Frontiers of Computer Vision (FCV), 2015 21st Korea-Japan Joint Workshop on
Conference_Location :
Mokpo
Type :
conf
DOI :
10.1109/FCV.2015.7103743
Filename :
7103743
Link To Document :
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