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Robust Video text detection with Morphological Filtering Enhanced MSER

Journal of Computer Science & Technology 2015年第2期30卷 353-363页

作者：诸葛云志卢湖川 College of Electronic Communication and Physics Shandong University of Science and Technology Qingdao 266510 China School of Information and Communication Engineering Dalian University of Technology Dalian 116023 China

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to video lossy compression and low resolution. In this paper, we propose a robust framework to solve these problems. Firstly, we exploit gradient amplitude map （GAM） to enhance the edge of an input image, which can overcome the problems of color bleeding and fuzzy boundaries. Secondly, a two-direction morphological filtering is developed to filter background noise and enhance the contrast between background and text. Thirdly, maximally stable extremal region （MSER） is applied to detect text regions with two extreme colors, and we use the mean intensity of the regions as the graph cuts＇ label set, and the Euclidean distance of three channels in HSI color space as the graph cuts smooth term, to get optimal segmentations. Finally, we group them into text lines using the geometric characteristics of the text, and then corner detection, multi-frame verification, and some heuristic rules are used to eliminate non-text regions. We test our scheme with some challenging videos, and the results prove that our text detection framework is more robust than previous methods.

关键词： text detection gradient amplitude map morphological filtering maximally stable extremM region graph cuts

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An enhanced text detection technique for the visually impaired to read text

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INFORMATION SYSTEMS FRONTIERS 2017年第5期19卷 1039-1056页

作者： Joan, S. P. Faustina Valli, S. Anna Univ Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

An enhanced text detection technique (ETDT) is proposed, which is expected to aid the visually impaired to overcome their reading challenges. This work enhances the edge-preserving maximally stable extremal regions (eMSER) algorithm using the pyramid histogram of oriented gradients (PHOG). Histogram of oriented gradients (HOG) derived from different pyramid levels is important while detecting maximally stable extremal regions (MSER) in the ETDT approach because it gives more spatial information when compared to HOG information from a single level. To group text, a four-line, text-grouping method is newly designed for this work. Also, a new text feature, Shapeness Score is proposed, which significantly identifies text regions when combined with the other features based on morphology and stroke widths. Using the feature vector of dimension 10, the J48 decision tree and AdaBoost machine learning algorithms identify the text regions in the images. The algorithm yields better results than the existing benchmark algorithms for the ICDAR 2011 born-digital dataset and must be improved with respect to the scene text dataset.

关键词： text detection MSER PHOG Shapeness score Stroke width

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SWT voting-based color reduction for text detection in natural scene images

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2013年第1期2013卷 1页

作者： Ikica, Andrej Peer, Peter Fac Comp & Informat Sci Comp Vis Lab Ljubljana Slovenia

In this article, we propose a novel stroke width transform (SWT) voting-based color reduction method for detecting text in natural scene images. Unlike other text detection approaches that mostly rely on either text structure or color, the proposed method combines both by supervising text-oriented color reduction process with additional SWT information. SWT pixels mapped to color space vote in favor of the color they correspond to. Colors receiving high SWT vote most likely belong to text areas and are blocked from being mean-shifted away. Literature does not explicitly address SWT search direction issue;thus, we propose an adaptive sub-block method for determining correct SWT direction. Both SWT voting-based color reduction and SWT direction determination methods are evaluated on binary (text/non-text) images obtained from a challenging Computer Vision Lab optical character recognition database. SWT voting-based color reduction method outperforms the state-of-the-art text-oriented color reduction approach.

关键词： text detection Natural scene images Stroke width transform SWT Color reduction SWT voting SWT direction determination SWT profiles CVL OCR DB

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text detection and Recognition in Real World Images

Text Detection and Recognition in Real World Images

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13th International Conference on Frontiers in Handwriting Recognition (ICFHR)

作者： Saabni, Raid Zwilling, Moti Tel Aviv Univ Fac Engn Triangle R&D Ctr Kafr Qarea Israel Acad Ctr Law Business Sch Business Adm Ramat Gan Israel

ISBN: (纸本)9780769547749;9781467322621

Detecting and recognizing texts in real world images such as sign boards and advertisements is an important part of computer vision applications. The complexity of the problem comes out of many factors such as non-uniform background, different languages and fonts, and non consistent text alignment and orientation. In this paper, we present a novel approach to detect characters and words in real-world images. The presented approach decompose the gray level image into sequence of images, each one includes pixels with gray level values from different disjoint ranges. This decomposition enables extracting connected components representing characters or other non textual objects separated from their neighborhood background. An interpolation of two classes of features translated to histograms is used by a support vector machine to classify and collect the textual objects generating the textual zones. The Shape Context Descriptor [1], is used by the Earth Movers Distance(EMD) method to recognize the characters within the image. The recognized characters are fed to heuristic rule based system to determine words and give final results. To optimize the speed of the system, we follow the embedding of the EMD metric presented in [22] to a normed space to enable fast approximation of the k-Nearest Neighbors using Local Sensitivity Hashing functions(LSH). Experiments show that our algorithm can detect and recognize text regions from the ICDAR 2005 datasets [17] with high rates.

关键词： Word Searching Earth Movers Distance Embedding Local Sensitivity Hashing k-Nearest Neighbor text detection

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text detection with convolutional neural networks

Text detection with convolutional neural networks

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3rd International Conference on Computer Vision Theory and Applications

作者： Delakis, Manolis Garcia, Christophe Orange Labs F-35512 Rennes France

ISBN: (纸本)9789898111210

text detection is an important preliminary step before text can be recognized in unconstrained image enviromments. We present an approach based on convolutional neural networks to detect and localize horizontal text lines from raw color pixels. The network learns to extract and combine its own set of features through learning instead of using hand-crafted ones. Learning was also used in order to precisely localize the text lines by simply training the network to reject badly-cut text and without any use of tedious knowledge-based post-processing. Although the network was trained with synthetic examples, experimental results demonstrated that it can outperform other methods on the real-world test set of ICDAR'03.

关键词： image understading neural networks text detection pattern recognition

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text detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey 21

Text Detection and Recognition by using CNNs in the Austro-H...

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6th International Workshop on Historical Document Imaging and Processing (HIP)

作者： Can, Yekta Said Kabadayi, M. Erdem Koc Univ Fac Social Sci Istanbul Turkey

ISBN: (纸本)9781450386906

Historical maps include precious data about historical, geographical and economic perspectives of a period. However, several unique challenges and opportunities accompany historical maps compared to modern maps, such as low-quality images, degraded manuscripts and the huge quantity of non-annotated digital map collections. In the recent decade, Convolutional Neural Networks (CNNs) are applied to solve various image processing problems, but they need enormous annotated data to have accurate results. In this work, we annotated text regions of the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 manually and made them accessible for researchers. Then, we detected the pixel-wise positions of text regions by employing the deep neural network architecture and recognized them with encouraging error rates.

关键词： Convolutional neural networks document analysis text recognition geospatial artificial intelligence text detection image processing

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Scene text detection and recognition： recent advances and future trends

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Frontiers of Computer Science 2016年第1期10卷 19-36页

作者： Yingying ZHU Cong YAO Xiang BAI School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan 430074 China

text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of vision-based ap- plications, therefore text detection and recognition in natu- ral scenes have become important and active research topics in computer vision and document analysis. Especially in re- cent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges （e.g. noise, blur, distortion, occlusion and varia- tion） still remain. The purposes of this survey are three-fold： 1） introduce up-to-date works, 2） identify state-of-the-art al- gorithms, and 3） predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.

关键词： text detection text recogntion natural image,algorithms applications

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Reinforcement Shrink-Mask for text detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 6458-6470页

作者： Yang, Chuang Chen, Mulin Yuan, Yuan Wang, Qi Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Existing real-time text detectors reconstruct text contours by shrink-masks only. Though they simplify the framework and can make the model run fast, the strong dependence on shrink-masks leads to unreliable detection results (e.g., miss detection and overdetection). Moreover, these methods ignore the information from surrounding pixels, which causes sensitive shrink-masks and accelerates the reliability decline of detection results. Considering the above problems, we construct an effective and efficient text detection network, termed as Reinforcement Shrink-Mask for text detection (RSMTD), which strengthens the model's ability to recognize texts while enjoying a high detection speed. Specifically, an effective text representation strategy (Reinforcement Shrink-Mask, RSM) is designed to decouple texts and shrink-masks. RSM builds texts through shrink-masks and reinforcement offsets to ensure stable detection results encountering shrink-masks that deviate from the ground-truth. It is worth noting that reinforcement offsets can force our method to focus on the foreground shapes to bring precise shrink-mask edges. For the robustness improvement of shrink-masks, Super-pixel Window (SPW) is proposed to encourage RSMTD to utilize the surroundings of each pixel to predict shrink-masks. Particularly, SPW treats the interval regions between texts and shrink-masks as background, which helps to suppress interval regions and to avoid text adhesion. Moreover, a lightweight feature merging branch is constructed to further accelerate the inference process. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.

关键词： Feature extraction Real-time systems Reliability Merging Shape Detectors Computational efficiency text detection arbitrary-shaped text real-time text detector

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Enabling Product Recognition and Tracking Based on text detection for Mobile Augmented Reality

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IEEE ACCESS 2022年 10卷 98769-98782页

作者： Hwang, Sangwon Lee, Jisun Kang, Seungwoo MAXST Seoul 06267 South Korea Kyung Hee Univ Smart Tourism Res Ctr Seoul 02447 South Korea Calif Baptist Univ Coll Hlth Sci Riverside CA 92504 USA KOREATECH Sch Comp Sci & Engn Cheonan 31253 Chungcheongnam South Korea

We propose a system that supports real-time product recognition and tracking based on text detection for mobile augmented reality. To accurately distinguish products with visually similar packages, we develop a method that recognizes product names by utilizing the characteristics of texts printed on the product packages. It first filters out irrelevant products and effectively ranks candidate products through an inverted index search. We significantly reduce processing overhead by selectively performing product name recognition. In addition, we present an optical-flow-based method that enables efficient and responsive product tracking. Our evaluation shows that the proposed system achieves significantly better product recognition accuracy (80%) compared to alternative solutions, Vuforia (55.4%) and MobileNetV2 (69.6%). We also show that it achieves reasonable tracking accuracy and processing latency to support quality mobile AR experiences.

关键词： text recognition Image recognition Target recognition Augmented reality Target tracking Feature extraction Real-time systems Character recognition Real-time systems Product delivery Mobile augmented reality product recognition product tracking text detection

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texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2003年第12期25卷 1631-1639页

作者： Kim, KI Jung, K Kim, JH Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea Sungkyunkwan Univ Inst Informat Commun & Technol Suwon 440746 Kyunggi Do South Korea

The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used;rather, the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regions are identified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) to the results of the texture analysis. The combination of CAMSHIFT and SVMs produces both robust and efficient text detection, as time-consuming texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed.

关键词： text detection image indexing texture analysis support vector machine CAMSHIFT

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