  Recent advances in deep neural networks (DNNs) have significantly enhanced
the capabilities of optical character recognition (OCR) technology, enabling
its adoption to a wide range of real-world applications. Despite this success,
DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the
adversary can influence the DNN model's prediction by carefully manipulating
input to the model. Prior work has demonstrated the security impacts of
adversarial attacks on various OCR languages. However, to date, no studies have
been conducted and evaluated on an OCR system tailored specifically for the
Thai language. To bridge this gap, this work presents a feasibility study of
performing adversarial attacks on a specific Thai OCR application -- Thai
License Plate Recognition (LPR). Moreover, we propose a new type of adversarial
attack based on the \emph{semi-targeted} scenario and show that this scenario
is highly realistic in LPR applications. Our experimental results show the
feasibility of our attacks as they can be performed on a commodity computer
desktop with over 90% attack success rate.
