Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing

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 occurs. For example, is account fraud, to avoid account fraud there was verification using ID card extraction using OCR and NLP. Optical Character Recognition (OCR) is a technology that used to generate text from images. With OCR we can extract Indonesian ID card or kartu tanda penduduk (KTP) into text using 3 different OCR libraries, PyOCR, Pytesseract, and TesseOCR. To improve the accuracy we made text corrections using Natural Language Processing (NLP) basic tools to fixing the text. With 50 Indonesian ID card image, we compared the performance with three different OCR libraries. The result of our experiment shows that Pytesseract had the best performance with 0.78 F-score and 4510 milliseconds to extract per ID card.