Abstract
This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group's threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database.
| Original language | English |
|---|---|
| Pages (from-to) | 555-575 |
| Number of pages | 21 |
| Journal | Journal of Information Science and Engineering |
| Volume | 35 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Hardware and Architecture
- Library and Information Sciences
- Computational Theory and Mathematics
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Automatic text recognition in natural scene using neural network classifier with dynamic-group-based hybrid particle swarm optimization. / Kuang-Hui, Tang; Huang, Chuan-Kuei; Cheng-Jian, Lin.
In: Journal of Information Science and Engineering, Vol. 35, No. 3, 01.01.2019, p. 555-575.Research output: Contribution to journal › Article
TY - JOUR
T1 - Automatic text recognition in natural scene using neural network classifier with dynamic-group-based hybrid particle swarm optimization
AU - Kuang-Hui, Tang
AU - Huang, Chuan-Kuei
AU - Cheng-Jian, Lin
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group's threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database.
AB - This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group's threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database.
UR - http://www.scopus.com/inward/record.url?scp=85065637423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065637423&partnerID=8YFLogxK
U2 - 10.6688/JISE.201905_35(3).0005
DO - 10.6688/JISE.201905_35(3).0005
M3 - Article
VL - 35
SP - 555
EP - 575
JO - Journal of Information Science and Engineering
JF - Journal of Information Science and Engineering
SN - 1016-2364
IS - 3
ER -