Recording of Law Enforcement Based on Court Decision Document Using Rule-based Information Extraction

Solihin, Firdaus; Budi, Indra

doi:10.1109/icacsis.2018.8618187

Cited by 7 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous research that also performed rule-based NER [11] has 89% F1-Score while our study has 86.27% F1-Score. The preceding experiment employed generated PDF documents with no OCR involves, while ours used scanned documents, and it has the worst text result after the OCR process.…”

Section: B Discussionmentioning

confidence: 52%

“…NER previous research on PDF documents like legal documents had been done to extract information for specific entities. Solihin and Budi, in 2018 [11], researched the extraction of data from general criminal court decision documents using the rule-based method. Meanwhile, Leitner et al in 2019 [12] used machine learning methods like CRF (Conditional Random Fields) and deep learning methods like BiLSTM (Bidirectional Long Short-Term Memory) to extract information using NER on legal documents.…”

Section: Related Workmentioning

confidence: 99%

“…Nuranti and Yulianti in 2020 [13] also tried the same method in Indonesian legal documents. Those research [11,12,13] used generated PDF legal documents as their data source, while our study used scanned PDF documents which may have lower accuracy influenced by OCR errors.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Optical Character Recognition Engines Performance Comparison in Information Extraction

Ramdhani

Budi²,

Purwandari³

2021

IJACSA

Self Cite

View full text Add to dashboard Cite

Named Entity Recognition (NER) is often used to acquire important information from text documents as a part of the Information Extraction (IE) process. However, the text documents quality affects the accuracy of the data obtained, especially for text documents acquired involving the Optical Character Recognition (OCR) process, which never reached 100% accuracy. This research tried to examine which OCR engine with the highest performance for IE using NER by comparing three OCR engines (Foxit, PDF2GO, Tesseract) over 8,562 government human resources documents within six document categories, two document structures, and four measurements. Several essential entities such as name, employee ID, document number, document publishing date, employee rank, and family member's name were trying to be extracted automatically from the documents. NER processes were done using Python programming language, and the preprocessing tasks were done separately for Foxit, PDF2GO, and Tesseract. In summary, each OCR engine has its drawbacks and benefit, such as Tesseract has better NER extraction and conversion time with better accuracy but lack in the number of entities acquired.

show abstract

Section: B Discussionmentioning

confidence: 52%

Section: Related Workmentioning

confidence: 99%