Text Extraction from Scrolling News Tickers

2020 ◽  
pp. 130-143
Author(s):  
Ingus Janis Pretkalnins ◽  
Arturs Sprogis ◽  
Guntis Barzdins
Keyword(s):  
Text Extraction

Text extraction algorithm based on binary clustering

2009 ◽  
Vol 29 (1) ◽  
pp. 57-59
Author(s):  
Wei DAI ◽  
Shen-sheng ZHANG
Keyword(s):  
Text Extraction ◽  
Extraction Algorithm

Automatic text extraction from color image

2000 ◽  
Author(s):  
WenPing Liu ◽  
Hui Su ◽  
Chang Y. Chi
Keyword(s):  
Color Image ◽  
Text Extraction ◽  
Automatic Text

Video-text extraction and recognition

2005 ◽  
Author(s):  
Teo Boon Chen ◽  
D. Ghosh ◽  
S. Ranganath
Keyword(s):  
Text Extraction

Effective and efficient video text extraction using key text points

2011 ◽  
Vol 5 (8) ◽  
pp. 671 ◽  
Author(s):  
Z. Li ◽  
G. Liu ◽  
X. Qian ◽  
D. Guo ◽  
H. Jiang
Keyword(s):  
Text Extraction ◽  
Efficient Video

Multi-Oriented Text Extraction in Stylistic Documents

2015 ◽  
Vol 15 (01) ◽  
pp. 1550002
Author(s):  
Brij Mohan Singh ◽  
Rahul Sharma ◽  
Debashis Ghosh ◽  
Ankush Mittal
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Morphological Operations ◽  
Document Images ◽  
Font Size ◽  
Text Extraction ◽  
Optical Character ◽  
Engineering Drawings ◽  
Flood Fill

In many documents such as maps, engineering drawings and artistic documents, etc. there exist many printed as well as handwritten materials where text regions and text-lines are not parallel to each other, curved in nature, and having various types of text such as different font size, text and non-text areas lying close to each other and non-straight, skewed and warped text-lines. Optical character recognition (OCR) systems available commercially such as ABYY fine reader and Free OCR, are not capable of handling different ranges of stylistic document images containing curved, multi-oriented, and stylish font text-lines. Extraction of individual text-lines and words from these documents is generally not straight forward. Most of the segmentation works reported is on simple documents but still it remains a highly challenging task to implement an OCR that works under all possible conditions and gives highly accurate results, especially in the case of stylistic documents. This paper presents dilation and flood fill morphological operations based approach that extracts multi-oriented text-lines and words from the complex layout or stylistic document images in the subsequent stages. The segmentation results obtained from our method proves to be superior over the standard profiling-based method.

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