FVI: An End-to-end Vietnamese Identification Card Detection and Recognition in Images

Liem, Hoang Danh; Minh, Nguyễn Đức; Trung, Nguyễn Bảo; Duc, Hoang Tien; Hiệp, Phạm Hoàng; Dung, Doan Viet; Vu, Duc-Ly

doi:10.1109/nics.2018.8606831

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…The main application of this technique has been to detect text in images. The OCR algorithm can be used to identify objects according to work by Adriano et al (2019), Deshpande and Shriram (2016), Liem et al (2018), and Liu et al ( 2016) who discuss how it could be used to accommodate more applications. Among these studies are experiments on food label detection, expiry date detection, and retrieving ID card information using the OCR-based model.…”

Section: Various Algorithms In Navigational Assistance For Visually I...mentioning

confidence: 99%

A Deep Learning Based Model to Assist Blind People in Their Navigation

Kumar

Jain

2022

JITE:IIP

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Aim/Purpose: This paper proposes a new approach to developing a deep learning-based prototyping wearable model which can assist blind and visually disabled people to recognize their environments and navigate through them. As a result, visually impaired people will be able to manage day-to-day activities and navigate through the world around them more easily. Background: In recent decades, the development of navigational devices has posed challenges for researchers to design smart guidance systems for visually impaired and blind individuals in navigating through known or unknown environments. Efforts need to be made to analyze the existing research from a historical perspective. Early studies of electronic travel aids should be integrated with the use of assistive technology-based artificial vision models for visually impaired persons. Methodology: This paper is an advancement of our previous research work, where we performed a sensor-based navigation system. In this research, the navigation of the visually disabled person is carried out with a vision-based 3D-designed wearable model and a vision-based smart stick. The wearable model used a neural network-based You Only Look Once (YOLO) algorithm to detect the course of the navigational path which is augmented by a GPS-based smart Stick. Over 100 images of each of the three classes, namely straight path, left path and right path, are being trained using supervised learning. The model accurately predicts a straight path with 79% mean average precision (mAP), the right path with 83% mAP, and the left path with 85% mAP. The average accuracy of the wearable model is 82.33% and that of the smart stick is 96.14% which combined gives an overall accuracy of 89.24%. Contribution: This research contributes to the design of a low-cost navigational standalone system that will be handy to use and help people to navigate safely in real-time scenarios. The challenging self-built dataset of various paths is generated and transfer learning is performed on the YOLO-v5 model after augmentation and manual annotation. To analyze and evaluate the model, various metrics, such as model losses, recall value, precision, and maP, are used. Findings: These were the main findings of the study: • To detect objects, the deep learning model uses a higher version of YOLO, i.e., a YOLOv5 detector, that may help those with visual im-pairments to improve their quality of navigational mobilities in known or unknown environments. • The developed standalone model has an option to be integrated into any other assistive applications like Electronic Travel Aids (ETAs) • It is the single neural network technology that allows the model to achieve high levels of detection accuracy of around 0.823 mAP with a custom dataset as compared to 0.895 with the COCO dataset. Due to its lightning-speed of 45 FPS object detection technology, it has become popular. Recommendations for Practitioners: Practitioners can help the model’s efficiency by increasing the sample size and classes used in training the model. Recommendation for Researchers: To detect objects in an image or live cam, there are various algorithms, e.g., R-CNN, Retina Net, Single Shot Detector (SSD), YOLO. Researchers can choose to use the YOLO version owing to its superior performance. Moreover, one of the YOLO versions, YOLOv5, outperforms its other versions such as YOLOv3 and YOLOv4 in terms of speed and accuracy. Impact on Society: We discuss new low-cost technologies that enable visually impaired people to navigate effectively in indoor environments. Future Research: The future of deep learning could incorporate recurrent neural networks on a larger set of data with special AI-based processors to avoid latency.

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Section: Various Algorithms In Navigational Assistance For Visually I...mentioning

confidence: 99%

A Deep Learning Based Model to Assist Blind People in Their Navigation

Kumar

Jain

2022

JITE:IIP

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“…Text recognition has been successfully used to extract information from the identity card (ID card) for different languages such as English, Chinese, Vietnamese, Spanish, etc. [3][4][5][6][7]. This study focuses on developing an approach to recognize Vietnamese text and an application to extract information from Vietnamese citizen identity card (CID carda.k.a.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some researchers have developed various deep learning approaches to Vietnamese text recognition for constrained forms [6][7][29][30]. Liem et al [6] proposed an FVI system for Vietnamese identification (ID) card detection and recognition. They first applied RetinaNet [31] to detect the ID card and text lines information.…”

Section: Introductionmentioning

confidence: 99%

An improved CRNN for Vietnamese Identity Card Information Recognition

Dat¹,

Dang²,

Truong³

et al. 2022

Computer Systems Science and Engineering

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This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity (CID) card. First, we apply Mask-RCNN to segment and align the CID card from the background. Next, we present two approaches to detect the CID card's text lines using traditional image processing techniques compared to the EAST detector. Finally, we introduce a new end-to-end Convolutional Recurrent Neural Network (CRNN) model based on a combination of Connectionist Temporal Classification (CTC) and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together. The length of the CTC's output label sequence is applied to the attention-based decoder prediction to make the final label sequence. This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference, instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences. We may directly learn the proposed model from a sequence of words without detailed annotations. We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28% in WER and outperforms the common techniques.

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“…with accuracy of about 90-95% [9,10]. In general, the recognition of Vietnamese text has been a matter of concern for many scientific researchers based on the different models and techniques such as using HANDS-VNOnDB [11], utilizing RetinaNet for text detection and Inception-v3 CNN network for feature extraction, passing through bidirectional long short-term memory (Bi-LSTM)-based RNN for text recognition [12], Bi-LSTM combined with Conditional Random Field (CRF) [13], SSD Mobilenet V2 for text detection and Attention OCR (AOCR) for text recognition [14], CNN, a well-known for image processing applications [15][16][17][18], integrated with grammatical features for emotion detection [16].…”

Section: Introductionmentioning

confidence: 99%

Vietnamese character recognition based on CNN model with reduced character classes

2021

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This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O”. Therefore, the authors built the classification model for 138 Vietnamese character classes after filtering out similar character classes to increase the model's effectiveness.

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FVI: An End-to-end Vietnamese Identification Card Detection and Recognition in Images

Cited by 11 publications

References 6 publications

A Deep Learning Based Model to Assist Blind People in Their Navigation

A Deep Learning Based Model to Assist Blind People in Their Navigation

An improved CRNN for Vietnamese Identity Card Information Recognition

Vietnamese character recognition based on CNN model with reduced character classes

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