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
텍스트 감지는 문서에서 글꼴과 필기 문자를 모두 포함한 텍스트를 정확하게 인식하기 위한 OCR(광학 문자 인식)의 중요한 전처리 단계입니다. 현재 딥러닝 기반 텍스트 감지 도구는 높은 정확도로 텍스트 영역을 감지할 수 있지만 여러 줄의 텍스트를 단일 영역으로 처리하는 경우가 많습니다. 줄 단위 문자 인식을 위해서는 텍스트를 개별 줄로 나누어야 하는데, 이를 위해서는 줄 감지 기술이 필요합니다. 이 문서에서는 기존 CRAFT(문자 영역 인식 텍스트 감지) 모델을 기반으로 하고 라인 분할에 특화된 심층 신경망을 통합하는 OCR의 단일 라인 감지에 대한 새로운 접근 방식의 개발에 중점을 둡니다. 그러나 이 새로운 방법은 간격이 좁은 여러 줄의 텍스트가 있는 경우에도 여러 줄을 단일 텍스트 영역으로 감지할 수 있습니다. 이 문제를 해결하기 위해 단일 라인 분할의 출력을 사용하여 단일 텍스트 영역을 감지하는 후처리 알고리즘도 도입합니다. 제안한 방법은 줄 간격이 좁은 여러 줄의 텍스트에서도 한 줄을 성공적으로 감지하므로 OCR의 정확도가 향상됩니다.
Chee Siang LEOW
University of Yamanashi
Hideaki YAJIMA
University of Yamanashi
Tomoki KITAGAWA
University of Yamanashi
Hiromitsu NISHIZAKI
University of Yamanashi
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Chee Siang LEOW, Hideaki YAJIMA, Tomoki KITAGAWA, Hiromitsu NISHIZAKI, "Single-Line Text Detection in Multi-Line Text with Narrow Spacing for Line-Based Character Recognition" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 12, pp. 2097-2106, December 2023, doi: 10.1587/transinf.2023EDP7070.
Abstract: Text detection is a crucial pre-processing step in optical character recognition (OCR) for the accurate recognition of text, including both fonts and handwritten characters, in documents. While current deep learning-based text detection tools can detect text regions with high accuracy, they often treat multiple lines of text as a single region. To perform line-based character recognition, it is necessary to divide the text into individual lines, which requires a line detection technique. This paper focuses on the development of a new approach to single-line detection in OCR that is based on the existing Character Region Awareness For Text detection (CRAFT) model and incorporates a deep neural network specialized in line segmentation. However, this new method may still detect multiple lines as a single text region when multi-line text with narrow spacing is present. To address this, we also introduce a post-processing algorithm to detect single text regions using the output of the single-line segmentation. Our proposed method successfully detects single lines, even in multi-line text with narrow line spacing, and hence improves the accuracy of OCR.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7070/_p
부
@ARTICLE{e106-d_12_2097,
author={Chee Siang LEOW, Hideaki YAJIMA, Tomoki KITAGAWA, Hiromitsu NISHIZAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Single-Line Text Detection in Multi-Line Text with Narrow Spacing for Line-Based Character Recognition},
year={2023},
volume={E106-D},
number={12},
pages={2097-2106},
abstract={Text detection is a crucial pre-processing step in optical character recognition (OCR) for the accurate recognition of text, including both fonts and handwritten characters, in documents. While current deep learning-based text detection tools can detect text regions with high accuracy, they often treat multiple lines of text as a single region. To perform line-based character recognition, it is necessary to divide the text into individual lines, which requires a line detection technique. This paper focuses on the development of a new approach to single-line detection in OCR that is based on the existing Character Region Awareness For Text detection (CRAFT) model and incorporates a deep neural network specialized in line segmentation. However, this new method may still detect multiple lines as a single text region when multi-line text with narrow spacing is present. To address this, we also introduce a post-processing algorithm to detect single text regions using the output of the single-line segmentation. Our proposed method successfully detects single lines, even in multi-line text with narrow line spacing, and hence improves the accuracy of OCR.},
keywords={},
doi={10.1587/transinf.2023EDP7070},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Single-Line Text Detection in Multi-Line Text with Narrow Spacing for Line-Based Character Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2097
EP - 2106
AU - Chee Siang LEOW
AU - Hideaki YAJIMA
AU - Tomoki KITAGAWA
AU - Hiromitsu NISHIZAKI
PY - 2023
DO - 10.1587/transinf.2023EDP7070
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2023
AB - Text detection is a crucial pre-processing step in optical character recognition (OCR) for the accurate recognition of text, including both fonts and handwritten characters, in documents. While current deep learning-based text detection tools can detect text regions with high accuracy, they often treat multiple lines of text as a single region. To perform line-based character recognition, it is necessary to divide the text into individual lines, which requires a line detection technique. This paper focuses on the development of a new approach to single-line detection in OCR that is based on the existing Character Region Awareness For Text detection (CRAFT) model and incorporates a deep neural network specialized in line segmentation. However, this new method may still detect multiple lines as a single text region when multi-line text with narrow spacing is present. To address this, we also introduce a post-processing algorithm to detect single text regions using the output of the single-line segmentation. Our proposed method successfully detects single lines, even in multi-line text with narrow line spacing, and hence improves the accuracy of OCR.
ER -