Martina Franchi, Stefania Colonnese, Alessia Cedola, Lia Barelli, Simona Morretta
{"title":"通过光度统计分析评估古纸碎片中文字的可读性","authors":"Martina Franchi, Stefania Colonnese, Alessia Cedola, Lia Barelli, Simona Morretta","doi":"10.1088/1748-0221/19/05/c05022","DOIUrl":null,"url":null,"abstract":"\n Ancient documents are important historical sources that are often found in a fragmented\n condition due to their conservation status. In this study, we examined fragments of paper found in\n 1996 during excavation of the\nSanti Quattro Coronati\ncomplex, in Rome. The archaeological site\n where the fragments were found is situated on the first floor of the tower within the\n complex. This location was used as a disposal pit approximately between the 15th and 16th\n centuries. The fragments exhibit text discoloration, hindering automatic recognition and human\n readability. To reveal the faded text, the fragments have been digitalized, converted into a\n perceptually uniform color space and the contrast has been enhanced. The photometric\n characteristics of the input and enhanced images have been statistically characterized, and the\n contrast enhancement assessed by a state-of-the-art metric. The statistical analysis of the text\n colour coordinates was carried out to develop supervised and unsupervised image segmentation,\n isolating the text.\n\nThe results of the method show that it effectively identifies text regions within images, improving readability, even for faded text. It can be integrated into deep learning-based character recognition systems, facilitating the automatic analysis of historical handwritten documents.","PeriodicalId":16184,"journal":{"name":"Journal of Instrumentation","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing readability of the text in ancient paper fragments by a photometric statistical analysis\",\"authors\":\"Martina Franchi, Stefania Colonnese, Alessia Cedola, Lia Barelli, Simona Morretta\",\"doi\":\"10.1088/1748-0221/19/05/c05022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Ancient documents are important historical sources that are often found in a fragmented\\n condition due to their conservation status. In this study, we examined fragments of paper found in\\n 1996 during excavation of the\\nSanti Quattro Coronati\\ncomplex, in Rome. The archaeological site\\n where the fragments were found is situated on the first floor of the tower within the\\n complex. This location was used as a disposal pit approximately between the 15th and 16th\\n centuries. The fragments exhibit text discoloration, hindering automatic recognition and human\\n readability. To reveal the faded text, the fragments have been digitalized, converted into a\\n perceptually uniform color space and the contrast has been enhanced. The photometric\\n characteristics of the input and enhanced images have been statistically characterized, and the\\n contrast enhancement assessed by a state-of-the-art metric. The statistical analysis of the text\\n colour coordinates was carried out to develop supervised and unsupervised image segmentation,\\n isolating the text.\\n\\nThe results of the method show that it effectively identifies text regions within images, improving readability, even for faded text. It can be integrated into deep learning-based character recognition systems, facilitating the automatic analysis of historical handwritten documents.\",\"PeriodicalId\":16184,\"journal\":{\"name\":\"Journal of Instrumentation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Instrumentation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1748-0221/19/05/c05022\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Instrumentation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1748-0221/19/05/c05022","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Assessing readability of the text in ancient paper fragments by a photometric statistical analysis
Ancient documents are important historical sources that are often found in a fragmented
condition due to their conservation status. In this study, we examined fragments of paper found in
1996 during excavation of the
Santi Quattro Coronati
complex, in Rome. The archaeological site
where the fragments were found is situated on the first floor of the tower within the
complex. This location was used as a disposal pit approximately between the 15th and 16th
centuries. The fragments exhibit text discoloration, hindering automatic recognition and human
readability. To reveal the faded text, the fragments have been digitalized, converted into a
perceptually uniform color space and the contrast has been enhanced. The photometric
characteristics of the input and enhanced images have been statistically characterized, and the
contrast enhancement assessed by a state-of-the-art metric. The statistical analysis of the text
colour coordinates was carried out to develop supervised and unsupervised image segmentation,
isolating the text.
The results of the method show that it effectively identifies text regions within images, improving readability, even for faded text. It can be integrated into deep learning-based character recognition systems, facilitating the automatic analysis of historical handwritten documents.
期刊介绍:
Journal of Instrumentation (JINST) covers major areas related to concepts and instrumentation in detector physics, accelerator science and associated experimental methods and techniques, theory, modelling and simulations. The main subject areas include.
-Accelerators: concepts, modelling, simulations and sources-
Instrumentation and hardware for accelerators: particles, synchrotron radiation, neutrons-
Detector physics: concepts, processes, methods, modelling and simulations-
Detectors, apparatus and methods for particle, astroparticle, nuclear, atomic, and molecular physics-
Instrumentation and methods for plasma research-
Methods and apparatus for astronomy and astrophysics-
Detectors, methods and apparatus for biomedical applications, life sciences and material research-
Instrumentation and techniques for medical imaging, diagnostics and therapy-
Instrumentation and techniques for dosimetry, monitoring and radiation damage-
Detectors, instrumentation and methods for non-destructive tests (NDT)-
Detector readout concepts, electronics and data acquisition methods-
Algorithms, software and data reduction methods-
Materials and associated technologies, etc.-
Engineering and technical issues.
JINST also includes a section dedicated to technical reports and instrumentation theses.