@inproceedings{86745175bff6492cad5d9f5c6aaaf58a,
title = "Split and Merge: Component Based Segmentation Network for Text Detection",
abstract = "This paper presents a novel component-based detector to locate scene texts with arbitrary orientations, shapes and lengths. Our approach detects text by predicting four components like text region (TR), text skeleton (TS), text sub-region (TSR) and text connector (TC). TR and TS can well separate adjacent text instance. TSR are merged by TC to form a complete text instance. Experimental results show that the proposed approach outperforms state-of-the-art methods on two curved text datasets, i.e. 82.42% and 82.63% F-measures were achieved for the Total-Text and CTW1500, respectively. Our approach also achieves competitive performance on multi-oriented dataset, i.e. 85.86% f-measure for the ICDAR2015 was achieved.",
keywords = "Arbitrary shape, Scene image, Text detection",
author = "Pan Gao and Qi Wan and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59830-3_2",
language = "English",
isbn = "9783030598297",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "14--27",
editor = "Yue Lu and Nicole Vincent and Yuen, {Pong Chi} and Wei-Shi Zheng and Farida Cheriet and Suen, {Ching Y.}",
booktitle = "Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings",
address = "Germany",
}