Neural Network-based System for Automatic Passport Stamp Classification - Archive ouverte HAL Access content directly
Journal Articles Information Technology And Control Year : 2020

Neural Network-based System for Automatic Passport Stamp Classification

(1) , (1) , (1) , (2, 3)


The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.
Fichier principal
Vignette du fichier
25919-Article Text-93555-1-10-20201219(1).pdf (8.28 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03101199 , version 1 (27-01-2021)


Attribution - CC BY 4.0



Wala Zaaboub, Lotfi Tlig, Mounir Sayadi, Basel Solaiman. Neural Network-based System for Automatic Passport Stamp Classification. Information Technology And Control, 2020, 49 (4), pp.583-607. ⟨10.5755/j01.itc.49.4.25919⟩. ⟨hal-03101199⟩
76 View
678 Download



Gmail Facebook Twitter LinkedIn More