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
최근 문자 감지기는 심층 신경망을 사용하여 모델링되었으며 자연 장면의 텍스트 감지, 역사적 문서의 문자 감지 등 다양한 작업에서 높은 성능을 달성했습니다. 그러나 기존 방법은 다양한 문자 크기 및 종횡비, 높은 문자 밀도 및 가까운 문자 간 거리로 인해 나무 전표에 대한 높은 감지 정확도를 달성할 수 없습니다. 본 연구에서는 문자 영역과 문자 간 경계를 학습하는 새로운 U-Net 기반 문자 검출 및 위치 파악 프레임워크를 제안합니다. 제안하는 방법은 문자 간 수직 및 수평 경계를 동시에 학습함으로써 문자 영역의 학습 성능을 향상시킨다. 또한, 학습된 문자 경계 영역을 활용하여 간단하고 저렴한 후처리 기능을 추가함으로써 가까운 동네에 있는 문자 그룹의 위치를 보다 정확하게 감지할 수 있습니다. 본 연구에서는 목제 전표 데이터세트를 구축합니다. 실험을 통해 제안된 방법이 역사적 문서에 대한 최첨단 문자 감지 방법을 포함하여 기존 문자 감지 방법보다 성능이 우수하다는 것을 입증했습니다.
Hojun SHIMOYAMA
Kansai University
Soh YOSHIDA
Kansai University
Takao FUJITA
Kansai University
Mitsuji MUNEYASU
Kansai University
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부
Hojun SHIMOYAMA, Soh YOSHIDA, Takao FUJITA, Mitsuji MUNEYASU, "U-Net Architecture for Ancient Handwritten Chinese Character Detection in Han Dynasty Wooden Slips" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 11, pp. 1406-1415, November 2023, doi: 10.1587/transfun.2023SMP0007.
Abstract: Recent character detectors have been modeled using deep neural networks and have achieved high performance in various tasks, such as text detection in natural scenes and character detection in historical documents. However, existing methods cannot achieve high detection accuracy for wooden slips because of their multi-scale character sizes and aspect ratios, high character density, and close character-to-character distance. In this study, we propose a new U-Net-based character detection and localization framework that learns character regions and boundaries between characters. The proposed method enhances the learning performance of character regions by simultaneously learning the vertical and horizontal boundaries between characters. Furthermore, by adding simple and low-cost post-processing using the learned regions of character boundaries, it is possible to more accurately detect the location of a group of characters in a close neighborhood. In this study, we construct a wooden slip dataset. Experiments demonstrated that the proposed method outperformed existing character detection methods, including state-of-the-art character detection methods for historical documents.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023SMP0007/_p
부
@ARTICLE{e106-a_11_1406,
author={Hojun SHIMOYAMA, Soh YOSHIDA, Takao FUJITA, Mitsuji MUNEYASU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={U-Net Architecture for Ancient Handwritten Chinese Character Detection in Han Dynasty Wooden Slips},
year={2023},
volume={E106-A},
number={11},
pages={1406-1415},
abstract={Recent character detectors have been modeled using deep neural networks and have achieved high performance in various tasks, such as text detection in natural scenes and character detection in historical documents. However, existing methods cannot achieve high detection accuracy for wooden slips because of their multi-scale character sizes and aspect ratios, high character density, and close character-to-character distance. In this study, we propose a new U-Net-based character detection and localization framework that learns character regions and boundaries between characters. The proposed method enhances the learning performance of character regions by simultaneously learning the vertical and horizontal boundaries between characters. Furthermore, by adding simple and low-cost post-processing using the learned regions of character boundaries, it is possible to more accurately detect the location of a group of characters in a close neighborhood. In this study, we construct a wooden slip dataset. Experiments demonstrated that the proposed method outperformed existing character detection methods, including state-of-the-art character detection methods for historical documents.},
keywords={},
doi={10.1587/transfun.2023SMP0007},
ISSN={1745-1337},
month={November},}
부
TY - JOUR
TI - U-Net Architecture for Ancient Handwritten Chinese Character Detection in Han Dynasty Wooden Slips
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1406
EP - 1415
AU - Hojun SHIMOYAMA
AU - Soh YOSHIDA
AU - Takao FUJITA
AU - Mitsuji MUNEYASU
PY - 2023
DO - 10.1587/transfun.2023SMP0007
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2023
AB - Recent character detectors have been modeled using deep neural networks and have achieved high performance in various tasks, such as text detection in natural scenes and character detection in historical documents. However, existing methods cannot achieve high detection accuracy for wooden slips because of their multi-scale character sizes and aspect ratios, high character density, and close character-to-character distance. In this study, we propose a new U-Net-based character detection and localization framework that learns character regions and boundaries between characters. The proposed method enhances the learning performance of character regions by simultaneously learning the vertical and horizontal boundaries between characters. Furthermore, by adding simple and low-cost post-processing using the learned regions of character boundaries, it is possible to more accurately detect the location of a group of characters in a close neighborhood. In this study, we construct a wooden slip dataset. Experiments demonstrated that the proposed method outperformed existing character detection methods, including state-of-the-art character detection methods for historical documents.
ER -