Indonesian ID Card Recognition using Convolutional Neural Networks

Pratama, Mahardhika; Satyawan, Wira; Fajar, Bagus; Fikri, Rusnandi; Hamzah, Haris

doi:10.1109/eecsi.2018.8752769

Cited by 7 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Works [17,18], and [19] describe systems for recognition of Indonesian identity cards. The workflow described in [17] is targeted on camera-captured documents, its processing steps include scaling, greyscaling and binarization of the document images, extracting of the text areas using connected component analysis, histogram-based per-character segmentation of text lines and template-based OCR.…”

Section: Related Workmentioning

confidence: 99%

“…The workflow described in [17] is targeted on camera-captured documents, its processing steps include scaling, greyscaling and binarization of the document images, extracting of the text areas using connected component analysis, histogram-based per-character segmentation of text lines and template-based OCR. In [18] the characters of Indonesian identity cards were recognized using CNNs (Convolutional Neural Networks) and SVMs (Support Vector Machines) with pre-processing. The system described in [19] includes smoothing as one of the image pre-processing steps, morphological operations for text fields detection and uses Tesseract [20] for text line recognition.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards a unified framework for identity documents analysis and recognition

Bulatov¹,

Bezmaternykh²,

Nikolaev

et al. 2022

Computer Optics

View full text Add to dashboard Cite

Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards a unified framework for identity documents analysis and recognition

Bulatov¹,

Bezmaternykh²,

Nikolaev

et al. 2022

Computer Optics

View full text Add to dashboard Cite

show abstract

“…A custom-built deep learning approach for text extraction from identity card images (Geerish Suddul) 35 through a Le-Net 5 [8] Convolutional neural network (CNN), achieving an accuracy of 99.2% in recognition and extraction of isolated characters. A further experiment by Pratama et al [9], demonstrated that it was possible to locate text regions on 10000 Indonesian ID Card by applying morphological operations such as dilation and erosion and then passing these text regions into a CNN to extract and recognize the characters in the text regions with an accuracy of 91%. A complete deep learning approach was adopted, by Ge et al [10], introducing a framework with two deep learning models for OCR on bank cards where the object detection algorithm YOLOV3 [11] with a DarkNet-53 backbone was used to predict bounding boxes corresponding bank identity number location on 1024 bank card images.…”

Section: Introductionmentioning

confidence: 99%

A custom-built deep learning approach for text extraction from identity card images

Suddul,

Seguin

2024

IJ-ICT

View full text Add to dashboard Cite

<span>Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to <span>detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.</span></span>

show abstract