DPAM: A New Deep Parallel Attention Model for Multiple License Plate Number Recognition

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Conference Article

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Proceedings - International Conference on Pattern Recognition


License plate number recognition is challenging for complex scenes containing multiple vehicles of different types, shapes, distances etc. To recognize multiple license plate numbers in an image, we propose a new model, called Deep Parallel Attention Model (DPAM), which simultaneously extracts unique features at character levels. The proposed model exploits the observation that the combination of alphanumeric characters does not have correlation at semantic level for extracting the features. This led to the introduction of parallelism for feature extraction at character levels to make it efficient in terms of time to fit in a real time environment. To test the proposed model, we consider our own dataset consisting of Indian license plate numbers and other standard datasets to show the superiority of the proposed model over the existing methods in terms of recognition rate. Furthermore, the proposed method is tested on scene text dataset to show its ability to detect text in natural scene images.

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