OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Singco, Van Zachary V.; Trillo, Joel C.; Abalorio, Cristopher C.; Bustillo, James Cloyd M.; Bojocan, Junell T.; Elape, Michelle C.

doi:10.46338/ijetae0223_07

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…For many diverse styles of programs in different fields, Optical character recognition is the bottom of it, which we use in our daily life. These days, Optical Character Recognition is being used in many different areas of research [12] [13]. P. Divya et al (2021) developed a web-based optical character recognition application using flask and tesseract [14].…”

Section: B Optical Character Recognitionmentioning

confidence: 99%

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Biol¹,

Depositario²,

Noangay³

et al. 2023

IJACSA

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Natural Language Processing, specifically text classification or text categorization, has become a trend in computer science. Commonly, text classification is used to categorize large amounts of data to allocate less time to retrieve information. Students, as well as research advisers and panelists, take extra effort and time in classifying research documents. To solve this problem, the researchers used state-of-the-art supervised term weighting schemes, namely: TF-MONO and SQRTF-MONO and its application to machine learning algorithms: K-Nearest Neighbor, Linear Support Vector, Naive Bayes Classifiers, creating a total of six classifier models to ascertain which of them performs optimally in classifying research documents while utilizing Optical Character Recognition for text extraction. The results showed that among all classification models trained, SQRTF-MONO and Linear SVC outperformed all other models with an F1 score of 0.94 both in the abstract and the background of the study datasets. In conclusion, the developed classification model and application prototype can be a tool to help researchers, advisers, and panelists to lessen the time spent in classifying research documents.

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Section: B Optical Character Recognitionmentioning

confidence: 99%

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Biol¹,

Depositario²,

Noangay³

et al. 2023

IJACSA

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show abstract

“…The transformer model revolution in Natural Language Processing [16] began with the paper "Attention Is All You Need" [17]. Before this, the state-of-the-art approach in NLP relied on gated recurrent neural networks such as Long Short-term Memory (LSTM) [18][19] [20]and gated recurrent units (GRUs) with added.…”

Section: Artificialmentioning

confidence: 99%

Optimizing Local Repository Search with Bidirectional Encoder Representations from Transformer Models

Escol,

Guibone,

Duenas

et al. 2023

IJETAE

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—In this research, we investigate the relationship between sentence transformer models and the accuracy and recall of asymmetric semantic search engines. Our results show that the chosen sentence transformer model has a significant impact on the performance of the search engine. Using a pre-trained model specific to the domain resulted in a high cosine similarity score of 0.64. Fine-tuning a pre-trained sentence transformer for asymmetric semantic search improved the score to 0.4, but a larger corpus was needed for optimal results. Our findings highlight the importance of careful model selection and training data in the development of asymmetric semantic search engines.

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Automatic depersonalization of confidential information

Babak,

Belorybkin,

Otsokov

et al. 2023

Rossijskij tehnologičeskij žurnal

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Objectives. As the scope of personal data transmitted online continues to grow, national legislatures are increasingly regulating the storage and processing of digital information. This paper raises the problem of protecting personal data and other confidential information such as bank secrecy or medical confidentiality of individuals. One approach to the protection of confidential data is to depersonalize it, i.e., to transform it so that it becomes impossible to identify the specific subject to whom the data belongs. The aim of the work is to develop a method for the rapid and safe automation of the depersonalization process using machine learning technologies.Methods. The authors propose the use of artificial intelligence models to implement a system for the automatic depersonalization of personal data without the use of human labor to preclude the possibility of recognizing confidential information even in unstructured data with sufficient accuracy. Rule-based algorithms for improving the precision of the depersonalization system are described.Results. In order to solve this problem, a model of named entity recognition is trained on confidential data provided by the authors. In conjunction with rule-based algorithms, an F1 score greater than 0.9 is achieved. For solving specific depersonalization problems, a choice between several implemented anonymization algorithm variants can be made.Conclusions. The developed system solves the problem of automatic anonymization of confidential data. This opens an opportunity to ensure the secure processing and transmission of confidential information in many areas, such as banking, government administration, and advertising campaigns. The automation of the depersonalization process makes it possible to transfer confidential information in cases where it is necessary, but not currently possible due to legal restrictions. The distinctive feature of the developed solution is that both structured data and unstructured data are depersonalized, including the preservation of context.

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OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document

Cited by 3 publications

References 31 publications

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Optimizing Local Repository Search with Bidirectional Encoder Representations from Transformer Models

Automatic depersonalization of confidential information

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