Automatic Information Extraction From Student ID Card Images Using DB and VietOCR: A Case Study at a Vietnamese University
Online First: 29/04/2026
Corressponding author's email:
nguyendung@hueuni.edu.vnDOI:
https://doi.org/10.54644/jte.2026.2101Keywords:
MobileNetV3, Differentiable Binarization, Text Detection, Vietnamese Text Recognition, VietOCRAbstract
The development of an information extraction system from student ID card images plays an important role in the digitalization of student management. This study proposes a two-stage processing framework that integrates computer vision and deep learning techniques, in which MobileNetV3-Small is employed for student identification card image classification, while the Differentiable Binarization (DB) model and VietOCR are responsible for Vietnamese text detection and recognition, respectively. Experimental results on a student ID card image dataset show that the classification model achieves an accuracy of 99.40% with an AUC of 0.9996, while the DB-based text detection model attains an Hmean of 89.81% after data augmentation. For text recognition, the proposed system achieves over 99% character-level accuracy and up to 98.90% full-sequence accuracy. These results demonstrate the effectiveness and practical feasibility of the proposed system, which is further validated through a proof-of-concept offline attendance application. In addition, the system is designed with computational efficiency in mind, enabling deployment on resource-constrained devices without requiring continuous internet connectivity. The proposed framework can be readily adapted to other types of identification documents, providing a scalable and cost-effective solution for automated data acquisition in educational institutions.
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