A brief review of document image retrieval methods: Recent advances

Alaei, Fahimeh; Alaei, Alireza; Blumenstein, Michael; Pal, Umapada

doi:10.1109/ijcnn.2016.7727648

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…Document image retrieval is normally used to find the appropriate document image from the database based on the user's queries. It has two approaches which include retrieval based on text recognition using optical character recognition (OCR) which relies on recognizing the text from the document and later examining the similarity as well as the retrieval without text recognition (not using OCR) which relies on image features in the document and later calculating the similarity with the actual content of the images [7]. This simply means the content-based document image retrieval method has 2 main stages which are the extraction of the text/text image feature and the search based on matching the text/text image [8].…”

Section: Introductionmentioning

confidence: 99%

Advanced content-based retrieval for digital correspondence documents with ontology classification

et al. 2022

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The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using a combination of convolutional neural network (CNN) and regular expression method), archiving (document archiving with automatic classification), and retrieval (content-based search by displaying ontology relationships from the document classification). The archiving of 100 documents using the automatic hierarchical classification was found to be 79% accurate as indicated by the 99% accuracy for CNN and 80% for Regex. Moreover, the search results for classified content-based documents through the display of ontology relationships were discovered to be 100% accurate. This research succeeded in improving the quality of search results for digital correspondence documents as indicated by its higher specificity, accuracy, and speed compared to conventional methods based on file names, annotations, and unclassified content.

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Section: Introductionmentioning

confidence: 99%

Advanced content-based retrieval for digital correspondence documents with ontology classification

et al. 2022

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“…For example, some databases offer a controlled vocabulary like a thesaurus or taxonomy from which to choose the search terms (e.g., the Medical Subject Headings [MeSH] in PubMed), whereas others offer a full text search. Regarding the latter, indexing scanned documents to offer a full text search, requires pre-processing methods like optical character recognition (OCR), known to include typos, and post-OCR processing, both affecting information retrieval accuracy [ 18 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

Almenara

2022

PLoS ONE

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Previous studies have used a query-based approach to search and gather scientific literature. Instead, the current study focused on domain-specific journals in the field of eating disorders. A total of 8651 documents (since 1981 to 2020), from which 7899 had an abstract, were retrieved from: International Journal of Eating Disorders (n = 4185, 48.38%), Eating and Weight Disorders (n = 1540, 17.80%), European Eating Disorders Review (n = 1461, 16.88%), Eating Disorders (n = 1072, 12.39%), and Journal of Eating Disorders (n = 393, 4.54%). To analyze these data, diverse methodologies were employed: bibliometrics (to identify top cited documents), network analysis (to identify the most representative scholars and collaboration networks), and topic modeling (to retrieve major topics using text mining, natural language processing, and machine learning algorithms). The results showed that the most cited documents were related to instruments used for the screening and evaluation of eating disorders, followed by review articles related to the epidemiology, course and outcome of eating disorders. Network analysis identified well-known scholars in the field, as well as their collaboration networks. Finally, topic modeling identified 10 major topics whereas a time series analysis of these topics identified relevant historical shifts. This study discusses the results in terms of future opportunities in the field of eating disorders.

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Section: Introductionmentioning

confidence: 99%

40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

Almenara¹

2022

Preprint

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Previous studies have used a query-based approach to search and gather scientific literature. Instead, the current study focused on domain-specific journals in the field of eating disorders. A total of 8651 documents (since 1981 to 2020) were retrieved from: International Journal of Eating Disorders (n = 4185, 48.38%), Eating and Weight Disorders (n = 1540, 17.80%), European Eating Disorders Review (n = 1461, 16.88%), Eating Disorders (n = 1072, 12.39%), and Journal of Eating Disorders (n = 393, 4.54%). To analyze these data, diverse methodologies were employed: bibliometrics (to identify top cited documents), network analysis (to identify the most representative scholars and collaboration networks), and topic modeling (to retrieve major topics using text mining, natural language processing, and machine learning algorithms). The results showed that the most cited documents were related to instruments used for the screening and evaluation of eating disorders, followed by review articles related to the epidemiology, course and outcome of eating disorders. Network analysis identified well-known scholars in the field, as well as their collaboration networks. Finally, topic modeling identified 10 major topics whereas a time series analysis of these topics identified relevant historical shifts. This study discusses the results in terms of future opportunities in the field of eating disorders.

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A brief review of document image retrieval methods: Recent advances

Cited by 7 publications

References 60 publications

Advanced content-based retrieval for digital correspondence documents with ontology classification

Advanced content-based retrieval for digital correspondence documents with ontology classification

40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling

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