Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images

Shivakumara, Palaiahnakote; Roy, Sambuddha; Jalab, Hamid A.; Ibrahim, Rabha W.; Pal, Umapada; Lü, Tong; Khare, Vijeta; Wahab, Ainuddin Wahid Abdul

doi:10.1016/j.eswa.2018.08.015

Cited by 17 publications

(8 citation statements)

References 15 publications

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“…Since the aim of the proposed work is to detect texts in night, day license plate images as well natural scene images, we consider license plate image datasets and the standard benchmark datasets of natural scene images in this work. However, since there is no standard dataset for night images, we created our own proposed method, we consider the standard datasets of day license plate images, namely, UCSD [8,34].…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

“…Similarly, Shivakumara et al [34] proposed a fractional means-based method for multi-oriented keyword spotting in video, natural scene and license plate images. However, the approach reports inconsistent results for images of different datasets.…”

Section: Related Workmentioning

confidence: 99%

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

Chowdhury

Shivakumara

Pal

et al. 2020

Multimed Tools Appl

Self Cite

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“…Canny edges extracted from the image and minimum cost path based ring growing have been used to restore missing text components. These features have been extracted locally and globally for spotting words from videos, natural scene, and license plate images (Shivakumara et al, 2018). However, these feature extraction methods are generally sensitive to noise.…”

Section: Spotting ‐Based Mining Approachesmentioning

confidence: 99%

“…Moreover, they are sensitive to font types and sizes, noise, distortion, and degradations. Therefore, these tasks and their pipeline as a whole, which is called the conventional model, may not provide good keyword spotting in the natural scene, and video frames (Shivakumara et al, 2018). Some methods in the literature used a recognition module for text spotting.…”

Section: Spotting ‐Based Mining Approachesmentioning

confidence: 99%

“…It is also worth mentioning that most of the descriptors are sensitive to contrast, background variations, and degradations which make the feature‐based text spotting method likely unreliable in images and video frames. The general problem with deep‐learning methods is the need for a large number of training samples that may result in the loss of their generic property (Mafla et al, 2020; Shivakumara et al, 2018). The use of pre‐trained models may be suitable to be used at different stages of the text spotting problem.…”

Section: Spotting ‐Based Mining Approachesmentioning

confidence: 99%

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Mining text from natural scene and video images: A survey

Shivakumara

Alaei

Pal

2021

WIREs Data Min & Knowl

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In computer terminology, mining is considered as extracting meaningful information or knowledge from a large amount of data/information using computers. The meaningful information can be extracted from normal text, and images obtained from different resources, such as natural scene images, video, and documents by deriving semantics from text and content of the images. Although there are many pieces of work on text/data mining and several survey/review papers are published in the literature, to the best of our knowledge there is no survey paper on mining textual information from the natural scene, video, and document images considering word spotting techniques. In this article, we, therefore, provide a comprehensive review of both the non‐spotting and spotting based mining techniques. The mining approaches are categorized as feature, learning and hybrid‐based methods to analyze the strengths and limitations of the models of each category. In addition, it also discusses the usefulness of the methods according to different situations and applications. Furthermore, based on the review of different mining approaches, this article identifies the limitations of the existing methods and suggests new applications and future directions to continue the research in multiple directions. We believe such a review article will be useful to the researchers to quickly become familiar with the state‐of‐the‐art information and progresses made toward mining textual information from natural scene and video images. This article is categorized under: Algorithmic Development > Text Mining

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Text Detection in Images

Shivakumara¹,

Pal²

2021

Cognitive Intelligence and Robotics

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Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images

Cited by 17 publications

References 15 publications

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

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

Mining text from natural scene and video images: A survey

Text Detection in Images

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