Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing
This work provides an incremental solution for service operators in Indonesia to automate data entry and reduce fraud by digitizing ID card information.
This paper addresses the problem of extracting information from Indonesian ID cards (KTP) to prevent account fraud and ease data entry. By combining OCR for text generation from images and NLP for text correction, the system achieved an F-score of 0.78 on 50 Indonesian ID card images, with an extraction time of 4510 milliseconds per card.
The development of Information Technology has been increasingly changing the means of information exchange leading to the need of digitizing print documents. In the present era, there is a lot of fraud that often occur. To avoid account fraud there was verification using ID card extraction using OCR and NLP. Optical Character Recognition (OCR) is technology that used to generate text from image. With OCR we can extract Indonesian ID card or kartu tanda penduduk (KTP) into text too. This is using to make easier service operator to do data entry. To improve the accuracy we made text correction using Natural language Processing (NLP) method to fixing the text. With 50 Indonesian ID card image we got 0.78 F-score, and we need 4510 milliseconds to extract per ID card.