A new multi-modal approach to bib number/text detection and recognition in Marathon images

Shivakumara, Palaiahnakote; Ramachandra, Raghavendra; Qin, Longfei; Raja, Kiran B.; Lü, Tong; Pal, Umapada

doi:10.1016/j.patcog.2016.08.021

Cited by 36 publications

(32 citation statements)

References 27 publications

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“…Disadvantage of this technique, the color based method is utilized which is very sensitive to lighting conditions. On the other hand, many features and classifiers were precisely used for detecting RBN by the multi-modal technique [6]. The detection accuracy was reported at 66%, tested on 212 images.…”

Section: Experimental Data Tools Results and Discussionmentioning

confidence: 99%

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Racing Bib Number Localization Based on Region Convolutional Neural Networks

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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This work aims to apply region convolutional neural network technique for detecting racing bib numbers on marathon images. Basically, marathon images are captured in the outdoor environment. The images are often ruined because of environment variations such as illumination, occlusion, and complex background. Transfer learning technique is applied for detecting a racing bib number on marathon images. Transfer learning algorithm firstly loads pre-trained model that was already trained on Cifar-10 image dataset. After that, racing bib number images (training images) are trained together with the pre-trained model into two classes: racing bib number class and non-racing bib number class. Finally, the trained model from the previous step is used to detect a racing bib number in marathon images. The results were reported to overtake previous research and F-measure was at 90 % on 400 marathon images.

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Section: Experimental Data Tools Results and Discussionmentioning

confidence: 99%

“…,28-30 June, 201828-30 June, , pp. 293-297 doi: 10.18178/wcse.2018.06.052[6] presented a combining technique which is also called multi-modal technique. The technique starts with torso detection.…”

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confidence: 99%

Racing Bib Number Localization Based on Region Convolutional Neural Networks

2018

Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering

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“…P Shivakumara et. al., [15] proposed a novel method for detection and recognition of text or bib number in marathon images by combining both torso and text detection.…”

Section: Sunil C K S Raghunandan H K Chethan G Hemantha Kumarmentioning

confidence: 99%

Deep Features based Multilingual Text Detection and Recognition in Video Frames

C*,

Raghunandan,

Chethan

et al. 2019

IJRTE

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 Abstract: One of the major causes for achieving poor text detection results in video frames are complex background, illumination and de-blurring of the frames and it is also important challenges for the researchers to overcome such problems. Therefore, motivated from this kind of observation from recent survey, we propose a text detection method based on Deep Neural Networks known as TextBoxes which is capable of detecting text in video frames with improved performance when compared to state-of-the-art techniques. In parallel this we also propose a Text candidate detection for video frames and scene images by extracting words based on Automatic Window Detection by making use of Discrete Wavelet Transform (DWT) with the sliding window for extracting high frequency sub bands for each sliding window. K-means clustering technique has been used to obtain the text components and to decrease the background complexity and noise. Six-layer convolutional neural network model has been designed to recognize the text in multilingual images. Experiments for text detection are done on our own multilingual South Indian database, ICDAR-2015 Videos, YVT videos, SVT, and MSRA Scene datasets and demonstrated in terms of Recall, Precision and F-measure and for recognition ICDAR-2015 Videos, ICDAR 2011 and SVT scene images.

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“…To reduce the number of false alarms they applied constraints on structural and temporal information on the candidate region before applying recognition method on them. Shivakumara et al [48] proposed a text detection approach using feature based on Pseudo-Zernike moments, Fourier and Polar descriptor followed by SVM based classification. Then text recognition was performed using OCR after binarization.…”

Section: Text Recognition From Scene Imagesmentioning

confidence: 99%

Text recognition in scene image and video frame using Color Channel selection

Bhunia

Kumar

Roy

et al. 2017

Multimed Tools Appl

Self Cite

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In recent years, recognition of text from natural scene image and video frame has got increased attention among the researchers due to its various complexities and challenges. Because of low resolution, blurring effect, complex background, different fonts, color and variant alignment of text within images and video frames, etc., text recognition in such scenario is difficult. Most of the current approaches usually apply a binarization algorithm to convert them into binary images and next OCR is applied to get the recognition result. In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames. In the approach, at first, a color channel is automatically selected and then selected color channel is considered for text recognition. Our text recognition framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram of Oriented Gradient features extracted from selected color channel. From each sliding window of a color channel our color-channel selection approach analyzes the image properties from the sliding window and then a multi-label Support Vector Machine (SVM) classifier is applied to select the color channel that will provide the best recognition results in the sliding window. This color channel selection for each sliding window has been found to be more fruitful than considering a single color channel for the whole word image. Five different features have been analyzed for multi-label SVM based color channel selection where wavelet transform based feature outperforms others. Our framework of color channel selection is script-independent. It has been tested in English (Roman) and Devanagari (Indic) scripts. We have tested our approach on English datasets (ICDAR 2003, ICDAR 2013 MSRA-TD500, IIIT5K, SVT, YVT) publicly available for both video and scene images. For Devanagari script, we collected our own dataset. The performances obtained from experimental results are encouraging and show the advantage of the proposed method.

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A new multi-modal approach to bib number/text detection and recognition in Marathon images

Cited by 36 publications

References 27 publications

Racing Bib Number Localization Based on Region Convolutional Neural Networks

Racing Bib Number Localization Based on Region Convolutional Neural Networks

Deep Features based Multilingual Text Detection and Recognition in Video Frames

Text recognition in scene image and video frame using Color Channel selection

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