The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Faster R-CNN은 단일 스케일 컨볼루션 필터와 완전히 연결된 네트워크로 구성된 영역 제안 네트워크를 사용하여 감지된 영역을 위치화합니다. 그러나 단일 스케일 필터를 사용하는 것만으로는 전체 문자 영역을 감지하는 데 충분하지 않습니다. 이 편지에서는 문서에서 다양한 크기의 한자를 정확하게 감지하기 위해 다양한 크기의 컨볼루션 필터를 활용하는 간단하면서도 효과적인 방법을 제안합니다. 우리는 기존의 단일 스케일 Faster R-CNN 방법에 비해 우리의 방법이 IoU를 4%, 탐지율을 3% 향상시키는 것을 실험적으로 검증했습니다.
Minseong KIM
Yeungnam University
Hyun-Chul CHOI
Yeungnam University
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부
Minseong KIM, Hyun-Chul CHOI, "Improving Faster R-CNN Framework for Multiscale Chinese Character Detection and Localization" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 7, pp. 1777-1781, July 2020, doi: 10.1587/transinf.2019EDL8217.
Abstract: Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8217/_p
부
@ARTICLE{e103-d_7_1777,
author={Minseong KIM, Hyun-Chul CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Faster R-CNN Framework for Multiscale Chinese Character Detection and Localization},
year={2020},
volume={E103-D},
number={7},
pages={1777-1781},
abstract={Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.},
keywords={},
doi={10.1587/transinf.2019EDL8217},
ISSN={1745-1361},
month={July},}
부
TY - JOUR
TI - Improving Faster R-CNN Framework for Multiscale Chinese Character Detection and Localization
T2 - IEICE TRANSACTIONS on Information
SP - 1777
EP - 1781
AU - Minseong KIM
AU - Hyun-Chul CHOI
PY - 2020
DO - 10.1587/transinf.2019EDL8217
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2020
AB - Faster R-CNN uses a region proposal network which consists of a single scale convolution filter and fully connected networks to localize detected regions. However, using a single scale filter is not enough to detect full regions of characters. In this letter, we propose a simple but effective way, i.e., utilizing variously sized convolution filters, to accurately detect Chinese characters of multiple scales in documents. We experimentally verified that our method improved IoU by 4% and detection rate by 3% than the previous single scale Faster R-CNN method.
ER -