Automatic text segmentation from complex background

2005 ◽  
Author(s):  
Qixiang Ye ◽  
Wen Gao ◽  
Qingming Huang
Keyword(s):  
Text Segmentation ◽  
Complex Background ◽  
Automatic Text

Text Segmentation from Bangla Land Map Images

2014 ◽  
Vol 19 (1) ◽  
pp. 21-34
Author(s):  
Samit Biswas ◽  
Amit Kumar Das ◽  
Bhabatosh Chanda
Keyword(s):  
Delaunay Triangulation ◽  
Spoken Language ◽  
Single Component ◽  
Text Segmentation ◽  
Spatial Form ◽  
Regional Language ◽  
Complex Background ◽  
The World ◽  
Segmentation Approach ◽  
Indic Scripts

Abstract Text segmentation from land map images is a non-trivial task as map components are interleaved and overlapped in a complex spatial form. The characters in a word in most of the Indic languages, including Bangla (the 6th most spoken language in the world), are connected through a headline (”matra” or ”shirorekha”) which makes the corresponding word a single component. It has been observed that the Delaunay triangulation (DT) forms a number of small triangles on the text regions compared to other regions of the map - a property very much discernible for Bangla (and some other Indic scripts) texts. This property is primarily exploited here to segment text from the complex background of the land map images. The proposed text segmentation approach is tested and compared with an existing method on a collected dataset of paper map images( containing Bangla, an Indian regional language texts) and the results are encouraging.

scholarly journals Automatic text segmentation and text recognition for video indexing

2000 ◽  
Vol 8 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Rainer Lienhart ◽  
Wolfgang Effelsberg
Keyword(s):  
Video Indexing ◽  
Text Recognition ◽  
Text Segmentation ◽  
Automatic Text

Textual Sentiment of Chinese Microblog Toward the Stock Market

2019 ◽  
Vol 18 (02) ◽  
pp. 649-671 ◽  
Author(s):  
Ning Wang ◽  
Shanhui Ke ◽  
Yibo Chen ◽  
Tao Yan ◽  
Andrew Lim
Keyword(s):  
Stock Exchange ◽  
Composite Index ◽  
Individual Investors ◽  
Text Segmentation ◽  
Segmentation Method ◽  
Sina Weibo ◽  
Collective Emotions ◽  
Portfolio Decision ◽  
The Relationship ◽  
Automatic Text

In this paper, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.

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