TY - GEN
T1 - Scene Text Relocation with Guidance
AU - Zhu, Anna
AU - Uchida, Seiichi
PY - 2018/1/25
Y1 - 2018/1/25
N2 - Applying object proposal technique for scene text detection becomes popular for its significant improvement in speed and accuracy for object detection. However, some of the text regions after the proposal classification are overlapped and hard to remove or merge. In this paper, we present a scene text relocation system that refines the detection from text proposals to text. An object proposal-based deep neural network is employed to get the text proposals. To tackle the detection overlapping problem, a refinement deep neural network relocates the overlapped regions by estimating the text probability inside, and locating the accurate text regions by thresholding. Since the spacebetweenwordsindifferenttextlinesarevarious, aguidance mechanism is proposed in text relocation to guide where to extract the text regions in word level. This refinement procedure helps boost the precision after removing multiple overlapped text regions or joint cracked text regions. The experimental results on standard benchmark ICDAR 2013 demonstrate the effectiveness of the proposed approach.
AB - Applying object proposal technique for scene text detection becomes popular for its significant improvement in speed and accuracy for object detection. However, some of the text regions after the proposal classification are overlapped and hard to remove or merge. In this paper, we present a scene text relocation system that refines the detection from text proposals to text. An object proposal-based deep neural network is employed to get the text proposals. To tackle the detection overlapping problem, a refinement deep neural network relocates the overlapped regions by estimating the text probability inside, and locating the accurate text regions by thresholding. Since the spacebetweenwordsindifferenttextlinesarevarious, aguidance mechanism is proposed in text relocation to guide where to extract the text regions in word level. This refinement procedure helps boost the precision after removing multiple overlapped text regions or joint cracked text regions. The experimental results on standard benchmark ICDAR 2013 demonstrate the effectiveness of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85045185878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045185878&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.212
DO - 10.1109/ICDAR.2017.212
M3 - Conference contribution
AN - SCOPUS:85045185878
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1289
EP - 1294
BT - Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
PB - IEEE Computer Society
T2 - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
Y2 - 9 November 2017 through 15 November 2017
ER -