  This paper explores the application of computer vision technologies to the
analysis of maps, an area with substantial historical, cultural, and political
significance. Our focus is on developing and evaluating a method for
automatically identifying maps that depict specific regions and feature
landmarks with designated names, a task that involves complex challenges due to
the diverse styles and methods used in map creation. We address three main
subtasks: differentiating maps from non-maps, verifying the accuracy of the
region depicted, and confirming the presence or absence of particular landmark
names through advanced text recognition techniques. Our approach utilizes a
Convolutional Neural Network and transfer learning to differentiate maps from
non-maps, verify the accuracy of depicted regions, and confirm landmark names
through advanced text recognition. We also introduce the VinMap dataset,
containing annotated map images of Vietnam, to train and test our method.
Experiments on this dataset demonstrate that our technique achieves F1-score of
85.51% for identifying maps excluding specific territorial landmarks. This
result suggests practical utility and indicates areas for future improvement.
