A New GVF Arrow Pattern for Character Segmentation from Double Line License Plate Images

Shivakumara, Palaiahnakote; Konwer, Aishik; Bhowmick, Abir; Khare, Vijeta; Pal, Umapada; Lü, Tong

doi:10.1109/acpr.2017.45

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…Since the proposed method consists of two steps, we conduct experiments on classification and recognition. For evaluating the proposed method, we consider live data given by the MIMOS company funded by Malaysian Government [16], which comprises 874 private images and 961 public images. This dataset includes images affected multiple adverse factors such as low resolution, blur and severe illumination effect, images with different background colour, and images affected by head light, speed of vehicles and so on.…”

Section: Resultsmentioning

confidence: 99%

CNN‐RNN based method for license plate recognition

Shivakumara

Tang

Asadzadehkaljahi

et al. 2018

CAAI Transactions on Intelligence Technology

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Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle (e.g., cars) have numbers with dark background, while public vehicle (taxis/cabs) have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks namely, BLSTM (Bi-Directional Long Short Term Memory), for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting (DCV), which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.

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Section: Resultsmentioning

confidence: 99%

CNN‐RNN based method for license plate recognition

Shivakumara

Tang

Asadzadehkaljahi

et al. 2018

CAAI Transactions on Intelligence Technology

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“…Since the problem is complex, dominant pixel detection alone is not adequate and hence it includes pixels that represent non-text information. It is noted that GVF provides the unique GVF pattern (GVF shows opposite arrow symmetry [42]) in the text portion. The proposed method extracts such an observation using GVF information for dominant pixels, resulting in candidate pixels.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Thus, this work presents a new method which explores gradient, R, G and B color spaces, Gradient Vector Flow (GVF), and tackles the challenges posed by night/day license plate images. To reduce the impact of day and night effects, motivated by the method in [42] where it is shown that gradient operation enhances low contrast edges, we explore the gradient operation in different way in this work. This is because the gradient operation not only sharpens low contrast edges but also avoids the problem of foreground and background color changes.…”

Section: Related Workmentioning

confidence: 99%

“…The concepts proposed in [8,40,42,43,44] are well-known and used in many applications. It has motivated us to employ these methods for license plate detection in night and day images in this work.…”

Section: Related Workmentioning

confidence: 99%

“…It has motivated us to employ these methods for license plate detection in night and day images in this work. It is noted from [42] that GVF symmetry is defined for character segmentation in day images but not license plate number detection in day and night images. Similarly, color spaces are used for low light image enhancement in [40,43] to improve text detection performance but not for both night and day images.…”

Section: Related Workmentioning

confidence: 99%

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A new augmentation-based method for text detection in night and day license plate images

Chowdhury

Shivakumara

Pal

et al. 2020

Multimed Tools Appl

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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-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed.

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Word and Character Segmentation

Shivakumara

Pal

2021

Cognitive Intelligence and Robotics

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A New GVF Arrow Pattern for Character Segmentation from Double Line License Plate Images

Cited by 6 publications

References 13 publications

CNN‐RNN based method for license plate recognition

CNN‐RNN based method for license plate recognition

A new augmentation-based method for text detection in night and day license plate images

Word and Character Segmentation

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