Mendeley
TY  _  JOUR
ID  -  1394090813579635
TI  -  Sub-Word Image Clustering in Old Printed Documents Using Template Matching
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Soheili M. R.
AU  -  Kabir E.
AD  -  دانشگاه تربیت مدرس
AD  -  دانشگاه تربیت مدرس
Y1  -  2014
PY  -  2014
VL  -  4
IS  -  
SP  -  85
EP  -  93
KW  -  Document image analysis
large document
incremental clustering
segmentation 
dataset
DO  -  
N2  -  Due to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications dealing with large uniform documents. We assumed that the whole document is printed in a single font and print quality is not good. To test our method, we created a dataset of all sub-words of a Farsi book. The book has 233 pages with more than 111000 sub-words manually labeled. We use an incremental clustering algorithm. Four simple features are extracted from each sub-word and compared with the corresponding features of each cluster center. If all features' differences lie within certain thresholds, the sub-word and the winner cluster center are finely compared using a template matching algorithm.  In our experiments, we show that all sub-words of the book are recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members.
UR  - rimag.ir/en/Article/28068
L1  - rimag.ir/en/Article/Download/28068
ER  -  
