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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 동국대학교 불교학술원 International Journal of Buddhist Thought and Culture International Journal of Buddhist Thought & Culture Vol.28 No.2
- 발행연도
- 2018.12
- 수록면
- 11 - 44 (34page)
- DOI
- 10.16893/IJBTC.2018.12.28.2.11
이용수
초록· 키워드
오류제보하기
Optical character recognition (OCR) - the fully automated transcription of text appearing in a digitized image - offers transformative opportunities for the scholarly study of written materials produced prior to the digital age. Digitization, in the sense of photographic reproduction, is now a well-established and efficiently performable mechanical process, and one with significant value in its own right for purposes of preservation as well as access to rare materials. As a result, hundreds of millions of pages of pre-modern Chinese works have already been digitized by libraries and academic institutions around the world - a significant portion of this increasingly being made freely available online.
To make use of this material efficiently, transcriptions of the textual content of these images are needed. Given the enormous volume of image data in existence - and its ongoing production as digitization continues - this task is only feasible if it can be fully automated: performed by software without manual human intervention. Individually, reliable transcriptions produced by OCR offer enormous time savings to researchers, enabling efficient navigation of materials in ways not possible without digital transcription. In aggregate, however, these transcriptions facilitate entirely new ways of exploring historical materials - for example, rapidly identifying material that one suspects might exist somewhere, without knowing in advance where that might actually be. It is also a prerequisite also to virtually any type of statistical analysis of these materials - the potential utility of which continues to increase as a larger and larger proportion of the extant corpus is transcribed.
This paper introduces a procedure for OCR of pre-modern Chinese written materials, both printed and handwritten, describing the complete process from digitized image through to automated transcription and manual correction of remaining errors, with particular attention to issues arising in this domain. The process described has been applied to over 25 million pages of pre-modern Chinese works, and the paper also introduces the Chinese Text Project (https://ctext.org) platform used to both make these results available to scholars as well as provide a distributed, crowdsourced mechanism for facilitating manual corrections at scale as well as further analysis of the materials.
To make use of this material efficiently, transcriptions of the textual content of these images are needed. Given the enormous volume of image data in existence - and its ongoing production as digitization continues - this task is only feasible if it can be fully automated: performed by software without manual human intervention. Individually, reliable transcriptions produced by OCR offer enormous time savings to researchers, enabling efficient navigation of materials in ways not possible without digital transcription. In aggregate, however, these transcriptions facilitate entirely new ways of exploring historical materials - for example, rapidly identifying material that one suspects might exist somewhere, without knowing in advance where that might actually be. It is also a prerequisite also to virtually any type of statistical analysis of these materials - the potential utility of which continues to increase as a larger and larger proportion of the extant corpus is transcribed.
This paper introduces a procedure for OCR of pre-modern Chinese written materials, both printed and handwritten, describing the complete process from digitized image through to automated transcription and manual correction of remaining errors, with particular attention to issues arising in this domain. The process described has been applied to over 25 million pages of pre-modern Chinese works, and the paper also introduces the Chinese Text Project (https://ctext.org) platform used to both make these results available to scholars as well as provide a distributed, crowdsourced mechanism for facilitating manual corrections at scale as well as further analysis of the materials.
목차
Abstract
Introduction
Image Pre-processing
Character Segmentation
Training Data Extraction
Language Modeling
Results
Using the Data
Crowdsourcing of Corrections
Conclusions and Future Work
References
참고문헌 (11)
참고문헌 신청
[학술대회논문] - Bloomberg, Dan S. / 1995 / Measuring Document Image Skew and Orientation / SPIE Conference : Document Recognition II : 302 ~ 316
[학술대회논문] - Breuel, Thomas M. / 2013 / High-Performance OCR for Printed English and Fraktur Using LSTM Networks / 12th International Conference on Document Analysis and Recognition (ICDAR 2013)
[단행본] - Causer, Tim / 2014 / Crowdsourcing our Cultural Heritage / Ashgate Publishing
[단행본] - Heijdra, Martin / 2006 / The Art of the Book in China / SOAS
[학술지(정기간행물)] - Moyle, Martin / 2011 / Manuscript Transcription by Crowdsourcing : Transcribe Bentham / Liber Quarterly 20 (3) : 347 ~ 356
[학술대회논문] - Breuel, Thomas M. / 2013 / High-Performance OCR for Printed English and Fraktur Using LSTM Networks / 12th International Conference on Document Analysis and Recognition (ICDAR 2013)
[단행본] - Causer, Tim / 2014 / Crowdsourcing our Cultural Heritage / Ashgate Publishing
[단행본] - Heijdra, Martin / 2006 / The Art of the Book in China / SOAS
[학술지(정기간행물)] - Moyle, Martin / 2011 / Manuscript Transcription by Crowdsourcing : Transcribe Bentham / Liber Quarterly 20 (3) : 347 ~ 356
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