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Automatic video superimposed text detection based on Nonsubsampled Contourlet Transform

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第6期77卷 7033-7049页

作者： Huang, Xiaodong Capital Normal Univ Beijing 100048 Peoples R China

Compared with other video semantic clues, such as gestures, motions etc., video text generally provides highly useful and fairly precise semantic information, the analysis of which can to a great extent facilitate video and scene understanding. It can be observed that the video texts show stronger edges. The Nonsubsampled Contourlet Transform (NSCT) is a fully shift-invariant, multi-scale, and multi-direction expansion, which can preserve the edge/silhouette of the text characters well. Therefore, in this paper, a new approach has been proposed to detect video text based on NSCT. First of all, the 8 directional coefficients of NSCT are combined to build the directional edge map (DEM), which can keep the horizontal, vertical and diagonal edge features and suppress other directional edge features. Then various directional pixels of DEM are integrated into a whole binary image (BE). Based on the BE, text frame classification is carried out to determine whether the video frames contain the text lines. Finally, text detection based on the BE is performed on consecutive frames to discriminate the video text from non-text regions. Experimental evaluations based on our collected TV videos data set demonstrate that our method significantly outperforms the other 3 video text detection algorithms in both detection speed and accuracy, especially when there are challenges such as video text with various sizes, languages, colors, fonts, short or long text lines.

关键词： Video text Nonsubsampled Contourlet Transform text detection text frame classification

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text detection IN VIDEO FRAMES USING HYBRID FEATURES

TEXT DETECTION IN VIDEO FRAMES USING HYBRID FEATURES

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International Conference on Machine Learning and Cybernetics

作者： Ji, Zhong Wang, Jian Su, Yu-Ting Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781424447053

text in video frames provides brief and important content information which is helpful to video scene understanding, annotation and searching. A new text detection method in video frames is proposed in this paper. First, a small overlapped sliding window is scanned over the frame from which hybrid features are extracted. And then SVM classifier is employed to distinguish the text from background. At last, vote mechanism and morphological filter are performed to precisely locate the text region. The new method is expected to outperform the existing strategies based on the following two improvements. One is selecting robust features to distinguish both the scene and overlay text from the complex backgrounds. The other is addressing the multilingual capability over the whole processing. The proposed algorithm has been evaluated by four different kinds of videos and the experiments show its high performance.

关键词： text detection Overlay text Scene text SVM Video annotation

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text detection in Nature Scene Images Using Two-stage Nontext Filtering 13

Text Detection in Nature Scene Images Using Two-stage Nontex...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Wang, Qingqing Lu, Yue Sun, Shiliang East China Normal Univ Dept Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (纸本)9781479918058

We present a text detection method in natural scene images based on two-stage nontext filtering. Firstly, we detect multi-channel maximally stable extremal regions (MSERs) as character candidates. To reduce the amount of repeating components, we merge the MSERs by choosing the most character-like ones when overlap happens. Then nontext components are filtered out by a two-stage labeling procedure, wherein we combine random forests with CRF. Finally, components labeled as text are grouped into words by an edge-cut strategy, and false positives are eliminated by a HOG-based classifier. The experimental results on the ICDAR2013 database show the effectiveness of the proposed method.

关键词： text detection MSERs CRF nontext filtering random forests edge-cut

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text detection and Recognition using Multiple Phase Method on Various Product Label for Visual Impaired People

Text Detection and Recognition using Multiple Phase Method o...

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International Conference on Sustainable Information Engineering and Technology (SIET)

作者： Rizdania Utaminingrum, Fitri Univ Brawijaya Fac Comp Sci FILKOM Malang Indonesia

ISBN: (纸本)9781538621820

This paper reviews one of a noteworthy objective in research range of computer vision and digital image processing, which are text detection process and text recognition process. text detection and text recognition are one of the popular research fields. The result of the study is useful for the visually impaired people, because it may help them in buying and choosing their preferred product in the market. This study applied Multiple Phase (MP) method for the text detection process and the text recognition process. The text detection was going through some phases which are the detection of Maximally Stable Extremal Regions (MSER), Canny edge detection, region filtering, and Optical Character Recognition (OCR). The OCR was used for text recognition process. The experimental result for our proposed method performance was 80.88%, which was better compared to the previous research which used the two-stage classifier that was only 69% of performance.

关键词： text detection text recognition Multiple Phase (MP) method visually impaired people

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text detection IN BORN-DIGITAL IMAGES USING MULTIPLE LAYER IMAGES

TEXT DETECTION IN BORN-DIGITAL IMAGES USING MULTIPLE LAYER I...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeng, Chao Jia, Wenjing He, Xiangjian Univ Technol Sydney Res Ctr Innovat IT Serv & Applicat iNEXT Sydney NSW 2007 Australia

ISBN: (纸本)9781479903566

In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.

关键词： Multiple layer image T-LBP maximum gradient difference text detection

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text detection Algorithm based on Improved YOLOv3 9

Text Detection Algorithm based on Improved YOLOv3

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IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Wang, Huibai Zhang, Zhenda North China Univ Technol Coll Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728111902

Focusing on scene text detection, this paper adopts an improved scene text detection algorithm based on YOLOv3 network. Firstly, for a single detection target, the training speed of Darknet53 backbone network used by YOLOv3 is slow due to too many layers, so that this paper proposes a method for replacing it by Darknet19. Secondly, multi-scale detection was retained in the original network, and three anchors of different sizes were used to predict the bounding boxes. The experiment results show that the training speed of this algorithm is greatly improved and the detection effect is accurate and stable.

关键词： convolutional neural network text detection target detection

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text detection in Images Based on Grayscale Decomposition and Stroke Extraction

Text Detection in Images Based on Grayscale Decomposition an...

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Chinese Conference on Pattern Recognition/1st CJK Joint Workshop on Pattern Recognition

作者： Fan, Wei Sun, Jun Katsuyama, Yutaka Hotta, Yoshinobu Naoi, Satoshi Fujitsu Res & Dev Ctr Co Ltd Beijing 100025 Peoples R China Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781424441990

A method of detecting text regions in images which combines grayscale decomposition and stroke extraction is proposed. By checking the consistency of the two text features, text-like connected components are grouped together to generate text line regions in the processed image. It shows good performance on efficiently detecting image text rendered in relatively complex backgrounds.

关键词： text detection grayscale decomposition stroke extraction

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text detection and Removal from Image using Inpainting with Smoothing

Text Detection and Removal from Image using Inpainting with ...

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International Conference on Pervasive Computing (ICPC)

作者： Wagh, Priyanka Deelip Patil, D. R. SESs RC Patel Inst Technol Dept Comp Engn Shirpur MH India

ISBN: (纸本)9781479962723

In this paper we have implemented a two stage frame work to remove unwanted text from images: first stage is to detect text from image and second stage is to remove that text using method of inpainting. To detect text, text localization and extraction is carried out followed by method of inpainting to fill the holes generated in image using surrounding region. To bring more efficiency in text detection smoothing works better. With the use of feature extraction, stroke filtering and centroid processing text detection is performed. Color histogram processing is carried out to carry clearer filtering of text components from other image components. In last stage, the text holes generated are filled with appropriate information present in same image using nearest matching neighborhood inpainting. To generate visually plausible region filling results, smoothing is carried out on the selected filled patches. We tested the implementation using different images, and compared the results of smoothing with results of implementation without smoothing. Experimental results show improved PSNR due to smoothing.

关键词： text detection text Localization Inpainting smoothing

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text detection in Medical Images Using Local Feature Extraction and Supervised Learning 12

Text Detection in Medical Images Using Local Feature Extract...

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ma, Yu Wang, Yuanyuan Fudan Univ Dept Elect Engn Shanghai Peoples R China Key Lab Med Imaging Comp & Comp Assisted Interven Shanghai Peoples R China

ISBN: (纸本)9781467376822

In this paper, a novel method to automatically detect the texts embedded in medical images is proposed. Specific local features for texts in medical images, such as local edge density, local intensity contrast, and connectivity, are defined and extracted to find out the candidate text regions. Then the histograms of oriented gradient (HOG) for all candidate regions are calculated. With both the HOG features and the aforementioned local features, an adaptive boosting (AdaBoost) classifier is used to discriminate the texts from non-text structures. Experimental results show that the proposed method has better text detection performance compared with previous methods. It can preserve the text information and eliminate the obstruction caused by different sources. The detected texts can provide additional information in many applications such as medical image retrieval.

关键词： text detection medical image local feature classification

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text detection in natural scenes with phase congruency approach 40

Text detection in natural scenes with phase congruency appro...

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Conference on Applications of Digital Image Processing XL

作者： Diaz-Escobar, Julia Kober, Vitaly CICESE Dept Comp Sci Ensenada 22860 BC Mexico Chelyabinsk State Univ Dept Math Chelyabinsk Russia

ISBN: (数字)9781510612501

ISBN: (纸本)9781510612501;9781510612495

In recent years, the importance of text detection in imagery has been increasing due to the great number of applications developed for mobile devices. text detection becomes complicated when backgrounds are complex or capture conditions are not controlled. In this work, a method for text detection in natural scenes is proposed. The method is based on the Phase Congruency approach, obtained via Scale-Space Monogenic signal framework. The proposed method is robust to geometrical distortions, resolution, illumination, and noise degradation. Finally, experimental results are presented using a natural scene dataset.

关键词： text detection monogenic scale-space phase-congruency non-uniform illumination

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