A new augmentation-based method for text detection in night and day license plate images
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A new augmentation-based method for text detection in night and day license plate images Pinaki Nath Chowdhury 1 & Palaiahnakote Shivakumara 2 & Umapada Pal 1 & Tong Lu 3 & Michael Blumenstein 4 Received: 16 October 2019 / Revised: 4 July 2020 / Accepted: 20 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/ license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed. Keywords Augmentation . Gradient vector flow . RGB color space . Text detection . License plate detection . License plate recognition
* Tong Lu [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Automatic driving, parking, toll point surveillance and security applications are the key areas that require special attention of researchers in the field of image processing and multimedia [27]. To make these applications successful, robust license plate detection is essential because license plate images captured for these applications pose many challenges, such as background variations, different heights or distances, low quality due to the dust, blur due to vehicle movements, illumination effect due to street lights etc. In addition, other challenges for license plate images captured during the night, such as poor quality due to low light and distortion i.e., from headlight illumination effects of many vehicles, make license plate detection mu
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