Object Recognition from Scientific Document Based on Compartment and Text Blocks Refinement Framework

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AWZQ73SKC" target="_blank" >RIV/00216208:11320/25:WZQ73SKC - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201806008&doi=10.1007%2fs42979-024-03130-7&partnerID=40&md5=5cb705228c8473330debf7c19e73addd" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201806008&doi=10.1007%2fs42979-024-03130-7&partnerID=40&md5=5cb705228c8473330debf7c19e73addd</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42979-024-03130-7" target="_blank" >10.1007/s42979-024-03130-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Object Recognition from Scientific Document Based on Compartment and Text Blocks Refinement Framework

Popis výsledku v původním jazyce

With the rapid development of the internet in the past decade, it has become increasingly important to extract valuable information from vast resources efficiently, which is crucial for establishing a comprehensive digital ecosystem, particularly in the context of research surveys and comprehension. The foundation of these tasks focuses on accurate extraction and deep mining of data from scientific documents, which are essential for building a robust data infrastructure. However, parsing raw data or extracting data from complex scientific documents have been ongoing challenges. Current data extraction methods for scientific documents typically use rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods can incur high coding costs for articles with intricate typesetting. Conversely, relying solely on machine learning methods necessitates annotation work for complex content types within the scientific document, which can be costly. Additionally, few studies have thoroughly defined and explored the hierarchical layout within scientific documents. The lack of a comprehensive definition of the internal structure and elements of the documents indirectly impacts the accuracy of text classification and object recognition tasks. From the perspective of analyzing the standard layout and typesetting used in the specified publication, we propose a new document layout analysis framework called Compartment and Text Blocks Refinement (CTBR). Firstly, we define scientific documents into hierarchical divisions: base domain, compartment, and text blocks. Next, we conduct an in-depth exploration and classification of the meanings of text blocks. Finally, we utilize the results of text block classification to implement object recognition within scientific documents based on rule-based compartment segmentation. For the experiment, we used the well-known ACL format proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% text block classification accuracy and 90% object recognition accuracy for tables and figures. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

Název v anglickém jazyce

Object Recognition from Scientific Document Based on Compartment and Text Blocks Refinement Framework

Popis výsledku anglicky

With the rapid development of the internet in the past decade, it has become increasingly important to extract valuable information from vast resources efficiently, which is crucial for establishing a comprehensive digital ecosystem, particularly in the context of research surveys and comprehension. The foundation of these tasks focuses on accurate extraction and deep mining of data from scientific documents, which are essential for building a robust data infrastructure. However, parsing raw data or extracting data from complex scientific documents have been ongoing challenges. Current data extraction methods for scientific documents typically use rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods can incur high coding costs for articles with intricate typesetting. Conversely, relying solely on machine learning methods necessitates annotation work for complex content types within the scientific document, which can be costly. Additionally, few studies have thoroughly defined and explored the hierarchical layout within scientific documents. The lack of a comprehensive definition of the internal structure and elements of the documents indirectly impacts the accuracy of text classification and object recognition tasks. From the perspective of analyzing the standard layout and typesetting used in the specified publication, we propose a new document layout analysis framework called Compartment and Text Blocks Refinement (CTBR). Firstly, we define scientific documents into hierarchical divisions: base domain, compartment, and text blocks. Next, we conduct an in-depth exploration and classification of the meanings of text blocks. Finally, we utilize the results of text block classification to implement object recognition within scientific documents based on rule-based compartment segmentation. For the experiment, we used the well-known ACL format proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% text block classification accuracy and 90% object recognition accuracy for tables and figures. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SN Computer Science

ISSN

2662-995X

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85201806008