Open Access
ARTICLE
An Attention-Based Recognizer for Scene Text
Yugang Li1, *, Haibo Sun1
1 Academy of Broadcasting Science, Beijing, 100866, China.
* Corresponding Author: Yugang Li. Email: .
Journal on Artificial Intelligence 2020, 2(2), 103-112. https://doi.org/10.32604/jai.2020.010203
Received 18 February 2020; Accepted 01 April 2020; Issue published 15 July 2020
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.
Keywords
Cite This Article
Y. Li and H. Sun, "An attention-based recognizer for scene text,"
Journal on Artificial Intelligence, vol. 2, no.2, pp. 103–112, 2020.