License Plate Recognition Based On Multi-Angle View Model
Dat Tran-Anh Khanh Linh Tran Hoai-Nam Vu
In the realm of research, the detection/recognition of text within
images/videos captured by cameras constitutes a highly challenging problem for
researchers. Despite certain advancements achieving high accuracy, current
methods still require substantial improvements to be applicable in practical
scenarios. Diverging from text detection in images/videos, this paper addresses
the issue of text detection within license plates by amalgamating multiple
frames of distinct perspectives. For each viewpoint, the proposed method
extracts descriptive features characterizing the text components of the license
plate, specifically corner points and area. Concretely, we present three
viewpoints: view-1, view-2, and view-3, to identify the nearest neighboring
components facilitating the restoration of text components from the same
license plate line based on estimations of similarity levels and distance
metrics. Subsequently, we employ the CnOCR method for text recognition within
license plates. Experimental results on the self-collected dataset
(PTITPlates), comprising pairs of images in various scenarios, and the publicly
available Stanford Cars Dataset, demonstrate the superiority of the proposed
method over existing approaches.