A blind deconvolution model for scene text detection and recognition in video

Khare, Vijeta; Shivakumara, Palaiahnakote; Raveendran, P.; Blumenstein, Michael

doi:10.1016/j.patcog.2016.01.008

Cited by 45 publications

(13 citation statements)

References 24 publications

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“…Many practical applications such as traffic sign reading, product recognition, intelligent inspection, and image searching, benefit from the rich semantic information of scene text. With the development of scene text detection methods [11,26,46,56], scene character recognition has emerged at the forefront of this research topic and is regarded as an open and very challenging research problem [45].…”

Section: Introductionmentioning

confidence: 99%

MORAN: A Multi-Object Rectified Attention Network for scene text recognition

Luo

Jin

Sun

2019

Pattern Recognition

417

239

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Section: Introductionmentioning

confidence: 99%

MORAN: A Multi-Object Rectified Attention Network for scene text recognition

Luo

Jin

Sun

2019

Pattern Recognition

417

239

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“…Roy et al [4] suggested an approach for identifying texts using binarization which is based on the concept of fusion. A blind deconvolutional model [50] was used for text detection by enhancing the edge information of the text regions. Here, classification between blurred and deblurred images was performed and it was followed by a deblurring operation which used Gaussian weighted-L1 for restoring sharpness of the edges in blurred images.…”

Section: Related Workmentioning

confidence: 99%

Multi-oriented text detection and verification in video frames and scene images

et al. 2018

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In this paper, we bring forth a novel approach of video text detection using Fourier-Laplacian filtering in the frequency domain that includes a verification technique using Hidden Markov Model (HMM). The proposed approach deals with the text region appearing not only in horizontal or vertical directions, but also in any other oblique or curved orientation in the image.Until now only a few methods have been proposed that look into curved text detection in video frames, wherein lies our novelty. In our approach, we first apply Fourier-Laplacian transform on the image followed by an ideal Laplacian-Gaussian filtering. Thereafter K-means clustering is employed to obtain the asserted text areas depending on a maximum difference map. Next, the obtained connected components (CC) are skeletonized to distinguish various text strings.Complex components are disintegrated into simpler ones according to a junction removal algorithm followed by a concatenation performed on possible combination of the disjoint skeletons to obtain the corresponding text area. Finally these text hypotheses are verified using HMM-based text/non-text classification system. False positives are thus eliminated giving us a robust text detection performance. We have tested our framework in multi-oriented text lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in video frames and scene texts.The results obtained show that proposed approach surpasses existing methods on text detection.

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“…However, these methods often show difficulties given the cases under variant blur. A handful of approaches are proposed to solve such a problem [22,23,24,25,26]. Gupta et al [22] propose a Motion Density Function to represent the camera motion which is further adopted to recover the spatially varying blur kernel.…”

Section: Image Deblurringmentioning

confidence: 99%

Learn to model blurry motion via directional similarity and filtering

Chen

et al. 2018

Pattern Recognition

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It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches

show abstract

A blind deconvolution model for scene text detection and recognition in video

Cited by 45 publications

References 24 publications

MORAN: A Multi-Object Rectified Attention Network for scene text recognition

MORAN: A Multi-Object Rectified Attention Network for scene text recognition

Multi-oriented text detection and verification in video frames and scene images

Learn to model blurry motion via directional similarity and filtering

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