Aligning document layouts extracted with different OCR engines with clustering approach

Tomovic, S.; Pavlovic, Kosta; Bajceta, M.

doi:10.1016/j.eij.2020.12.004

Cited by 6 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an important step in extracting information contained in image documents using the OCR algorithm. Extraction of information from scanned documents is highly dependent on the position of fields in the document, OCR algorithms can handle administrative documents with complex layouts [12].…”

Section: Image Processing and Ocrmentioning

confidence: 99%

Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Multazam

Qashlim

Sarjan

2023

JSG

View full text Add to dashboard Cite

Extracting book metadata by retyping the identity of the book, such as the author's name, book title, publisher, and several other identities, is a routine that is carried out repeatedly at the Polewali Mandar district library, this activity takes much of time, using several staff and it turns out that this activity has much potential for input errors. Errors in extracting book metadata will result in errors in the book repository system database, resulting in difficulty finding and using books or book data information. This problem can be solved by creating a book metadata extraction system using image processing technology and OCR. This study aims to design a scanner technology to extract book metadata. Accuracy is carried out in 2 stages, the first validation of image extraction results using the ROC method and the second validation by directly matching the result of extracting the book's metadata with the actual book. The results of this study indicate that the system has worked with an accuracy of 98.78% with an average detection time of 1.49 seconds and has succeeded in presenting the extraction results on the website page. Thus the metadata extraction system with the OCR method can be applied to libraries to input book data.

show abstract

Section: Image Processing and Ocrmentioning

confidence: 99%

Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Multazam

Qashlim

Sarjan

2023

JSG

View full text Add to dashboard Cite

show abstract

“…This system has great potential to improve the accuracy and speed of text input on mobile devices, which could significantly enhance the user experience in a variety of applications. Finally, it was analyzed that in [12] performed image processing on structured and unstructured documents, managing to implement an alignment algorithm to be used by any optical character recognition engine. Therefore, the study proposes a comparative approach for automatic alignment of documents in environments where multiple OCR engines are used.…”

Section: Related Workmentioning

confidence: 99%

Mobile application to digitize handwritten patient records in peruvian public hospitals

Romero Villa,

Gregorini Machuca,

Copaja Cornejo

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

View full text Add to dashboard Cite

This research has shown that public hospitals need a digitization model that allows the availability of medical records for patient medical consultation within a repository that unifies their medical information. This article presents MEDREC APP, a mobile application aimed at public hospitals of the Ministry of Health (MINSA), in order to expedite the immediate obtaining of the medical history in digital format, and to visualize their medical care. The main function of this solution is based on Optical Character Recognition (OCR) as a text extraction process by capturing images of handwritten medical history formats and registering them within the mobile application in digital format. As part of the validation process, indicators were defined, surveys and interviews were conducted with our users of the application: the medical staff. The results obtained show that the average time to obtain a medical history is between 30 and 15 minutes. By including the mobile solution to be presented, the time was reduced to 6 minutes, which means a reduction of 9 minutes, equivalent to approximately 70%.

show abstract

Extraction of dimension requirements from engineering drawings for supporting quality control in production processes

Scheibel

Mangler

Rinderle-Ma

2021

Computers in Industry

View full text Add to dashboard Cite

Aligning document layouts extracted with different OCR engines with clustering approach

Cited by 6 publications

References 14 publications

Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Mobile application to digitize handwritten patient records in peruvian public hospitals

Extraction of dimension requirements from engineering drawings for supporting quality control in production processes

Contact Info

Product

Resources

About