@Article{jai.2020.010203,
AUTHOR = {Yugang Li, Haibo Sun},
TITLE = {An Attention-Based Recognizer for Scene Text},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {103--112},
URL = {http://www.techscience.com/jai/v2n2/39518},
ISSN = {2579-003X},
ABSTRACT = {Scene text recognition (STR) is the task of recognizing character sequences in 
natural scenes. Although STR method has been greatly developed, the existing methods 
still can't recognize any shape of text, such as very rich curve text or rotating text in daily 
life, irregular scene text has complex layout in two-dimensional space, which is used to 
recognize scene text in the past Recently, some recognizers correct irregular text to 
regular text image with approximate 1D layout, or convert 2D image feature mapping to 
one-dimensional feature sequence. Although these methods have achieved good 
performance, their robustness and accuracy are limited due to the loss of spatial 
information in the process of two-dimensional to one-dimensional transformation. In this 
paper, we proposes a framework to directly convert the irregular text of two-dimensional 
layout into character sequence by using the relationship attention module to capture the 
correlation of feature mapping Through a large number of experiments on multiple 
common benchmarks, our method can effectively identify regular and irregular scene text, 
and is superior to the previous methods in accuracy.},
DOI = {10.32604/jai.2020.010203}
}