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text localization in camera captured images using fuzzy distance transform based adaptive stroke filter

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第13期78卷 18017-18036页

作者： Paul, Shauvik Saha, Satadal Basu, Subhadip Saha, Punam Kumar Nasipuri, Mita Techno India MCA Dept Kolkata India MCKV Inst Engn ECE Dept Howrah India Jadavpur Univ CSE Dept Kolkata India Univ Iowa Dept Radiol Iowa City IA 52242 USA

localization of text from camera captured images with complex background is now-a-days a growing demand of modern IT enable service. Most of the current text localization techniques are sensitive to text features like color, size, style and also to the background clutter. Among all the methods proposed in different literatures, Stroke Filter is much more effective in localization of text. The effectiveness of traditional stroke filter is limited because of its fixed width and is capable of segmenting strokes/texts of predefined range of width. The proposed method uses Fuzzy Distance Transform based adaptive stroke filter which can effectively localize text regions from camera captured images with complex background. The method is applied by experiment on a database containing 600 images and the visual response of text segmentation is quite impressive. To get the accuracy of the proposed method, it is applied on a set of 16 test images and the segmentation result is compared with the ground truth images resulting in a recall, precision and f-measure values of 96.65%, 87.77% and 91.89% respectively.

关键词： Collision impact Fuzzy distance transform Quench pixel Skeletonization Stroke filter text localization

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text localization in digital images using a hybrid method

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 34047-34066页

作者： Akoushideh, Alireza Rasoulnejad, Sayed Mohammad Fallah Shahbahrami, Asadollah Tech & Vocat Univ TVU Guilan Branch Fac Shahid Chamran Dept Elect Engn Rasht Iran Lahijan Islamic Azad Univ Dept Comp Sci Lahijan Iran Univ Guilan Fac Engn Dept Comp Engn Rasht Iran

text localization in digital images includes various applications such as routing in auto-driving, postal services, identifying containers in ports, robotic navigation in urban environments, and so on. Multiple methods such as Stroke Width Transform (SWT), Local Adaptive Thresholding (LAT), and Maximally Stable Extremal Regions (MSER) have already been noticed by many researchers. These methods address specific challenges such as text orientation, font variations, size, and non-uniform illuminations. Additionally, deep methods with high accuracy are not optimal regarding computation time and data required for training. Our research considers the SWT, LAT, and MSER methods as a pre-processing step with some improvement techniques to simultaneously deal with the challenges above and improve the text localization result. Maximally Stable Extremal Regions, multilevel binarization, and non-maximum suppression techniques have been used in our proposed approach. Our proposed approach has reasonable results compared with the none-deep state-of-the-art algorithms and deep learning-based methods on the ICDAR 2013 dataset. Recall, precision, and f-measure are 0.7442, 0.9116, and 0.8195, respectively.

关键词： text localization Stroke width transform Local adaptive thresholding Maximally stable extremal regions text detection

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text localization and character segmentation algorithms for automatic recognition of slab identification numbers

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OPTICAL ENGINEERING 2009年第3期48卷 037206-1-037206-1页

作者： Choi, SungHoo Yun, Jong Pil Kim, Sang Woo Pohang Univ Sci & Technol POSTECH Dept Elect & Elect Engn Pohang 790784 South Korea

This paper describes application-oriented text localization and character segmentation algorithms in images. The target text in our application includes many unclear characters due to poor environment as well as the fact that their positions are variable in the images. Consequently, it is difficult to expect a high success rate when using existing text localization algorithms that have been developed for generic texts. Therefore, it is necessary to develop a new text localization algorithm. We propose (1) a coarse algorithm for detecting top and bottom boundaries, (2) a fitness function that is used to decide the true text among the text candidates, (3) two kinds of presegmentation algorithms for calculating the fitness function, and (4) a blank-detecting algorithm that determines whether the text is upside down or not. By the proposed algorithms, input upside-down text is rotated automatically without using any supervised or unsupervised learning methods;further, character segmentation can be done in the process of selecting the true text. To evaluate the algorithms, image data captured by the installed recognition system at Pohang Steel Company (POSCO) are used, and experimental results show that the proposed algorithms are fast and reliable. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3083340]

关键词： text detection text localization text extraction text finder character location character segmentation optical character recognition

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text localization in color documents

Text localization in color documents

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1st International Conference on Computer Vision Theory and Applications

作者： Nikolaou, N. Badekas, E. Papamarkos, N. Strouthopoulos, C. Democritus Univ Thrace Dept Elect & Comp Engn Image Proc & Multimedia Lab GR-67100 Xanthi Greece Technol Educ Inst Serres Dept Informat & Commun Serres 62123 Greece

ISBN: (纸本)9728865406

A new method for text localization in cover color pages and general color document images is presented. The colors of the document image are reduced to a small number using a color reduction technique based on a Kohonen Self Organized Map (KSOM) neural network. Each color defines a color plane in which the connected components (CCs) are extracted. In each color plane a CC filtering procedure is applied which is followed by a local grouping procedure. At the end of this stage, groups of CCs are constructed which are next refined by obtaining the Direction of Connection (DOC) property for each CC. Using the DOC property, the groups of CCs are classified as text or non text regions. Finally, text regions identified in the different color planes are superimposed and the final text localization of the entire document is achieved. The proposed technique was extensively tested with a large number of color documents.

关键词： Color quantization Document processing Page layout analysis text localization

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text localization in Video/Scene Images using Kirsch Directional Masks

Text Localization in Video/Scene Images using Kirsch Directi...

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International Conference on Advances in Computing, Communications and Informatics ICACCI

作者： Shekar, B. H. Smitha, M. L. Mangalore Univ Dept Comp Sci Mangalore Karnataka India KVG Coll Engn Dept Master Comp Applicat Sullia Karnataka India

ISBN: (纸本)9781479987924

text plays its vital role in visual content analysis and understanding. Videos contain text with diversity in its text patterns and complex backgrounds. In this paper, we propose an approach based on compass operator for detecting the edges. We obtain the edge maps by convolving the Kirsch Directional Masks along eight different directions for the preprocessed video frame. The resultant images are binarized and the edge maps are analyzed and concatenated to obtain a single binarized image. Further, we employ geometrical rules and shape properties to eliminate the false positives. Then, the text regions are identified using connected component analysis and bounding boxes are placed for the detected text regions so that the text gets localized. Experimental results obtained on the standard datasets reveal that the proposed method effectively detect and localize texts of various sizes, fonts and colors in videos and scene images.

关键词： Kirsch Operator text Detection text localization Video Analysis

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text localization in natural scene images using multiple classifiers

Text localization in natural scene images using multiple cla...

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Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007

作者： Kozlowski, Adam Strumillo, Pawel Tech Univ Lodz Inst Elect PL-90924 Lodz Poland

ISBN: (纸本)9780819471246

localization of text areas in images of natural scenes is essential for many applications, including support for the blind, tourist support, traffic control and content-sensitive image or video databases. Algorithms for localization of scene text must overcome issues of non-uniform scene lighting, scene text variations and distortions etc. In the paper we present the results obtained for scene text detection algorithms operating on various image representation domains, as well as our work on an algorithm based on an ensemble of simple classifiers.

关键词： text localization text detection natural scene image analysis blind support

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text localization in Real-world Images using Efficiently Pruned Exhaustive Search

Text Localization in Real-world Images using Efficiently Pru...

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11th International Conference on Document Analysis and Recognition (ICDAR)

作者： Neumann, Lukas Matas, Jiri Czech Tech Univ Dept Cybernet Ctr Machine Percept CR-16635 Prague Czech Republic

ISBN: (纸本)9780769545202

An efficient method for text localization and recognition in real-world images is proposed. Thanks to effective pruning, it is able to exhaustively search the space of all character sequences in real time (200ms on a 640x480 image). The method exploits higher-order properties of text such as word text lines. We demonstrate that the grouping stage plays a key role in the text localization performance and that a robust and precise grouping stage is able to compensate errors of the character detector. The method includes a novel selector of Maximally Stable Extremal Regions (MSER) which exploits region topology. Experimental validation shows that 95.7% characters in the ICDAR dataset are detected using the novel selector of MSERs with a low sensitivity threshold. The proposed method was evaluated on the standard ICDAR 2003 dataset where it achieved state-of-the-art results in both text localization and recognition.

关键词： text localization real-world images text-in-the-wild

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text localization in Web Images Using Probabilistic Candidate Selection Model

Text Localization in Web Images Using Probabilistic Candidat...

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11th International Conference on Document Analysis and Recognition (ICDAR)

作者： Situ, Liangji Liu, Ruizhe Tan, Chew Lim Natl Univ Singapore Sch Comp Singapore 117548 Singapore

ISBN: (纸本)9780769545202

Web has become increasingly oriented to multimedia content. Most information on the web is conveyed from images. text localization in web image plays an important role in web image information extraction and retrieval. Current works on text localization in web images assume that text regions are in homogenous color and high contrast. Hence, the approaches may fail when text regions are in multi-color or imposed in complex background. In this paper, we propose a text extraction algorithm from web images based on the probabilistic candidate selection model. The model firstly segments text region candidates from input images using wavelet, Gaussian mixture model (GMM) and triangulation. The likelihood of a candidate region containing text is then learnt using a Bayesian probabilistic model from two features, namely, histogram of oriented gradient (HOG) and local binary pattern histogram Fourier feature (LBP-HF). Finally best candidate regions are integrated to form text regions. The algorithm is evaluated using 155 non-homogenous web images containing around 600 text regions. The results show that the proposed model is able to extract text regions from non-homogenous images effectively.

关键词： text extraction text localization web image

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text localization with Hierarchical Multiple Feature Learning 16th

Text Localization with Hierarchical Multiple Feature Learnin...

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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Qu, Yanyun Lin, Li Liao, Weiming Liu, Junran Wu, Yang Wang, Hanzi Xiamen Univ Dept Comp Sci Xiamen Peoples R China Nara Inst Sci Technol Ctr Frontier Sci & Technol Nara Japan

ISBN: (纸本)9783319240756;9783319240749

In this paper, we focus on English text localization in natural scene images. We propose a hierarchical localization framework which goes from characters to strings to words. Different from existing methods which either bet on sophisticated hand-crafted features or rely on heavy learning models, our approach tends to design simple but effective features and learning models. In this study, we introduce a kind of two level character structure features in collaboration with the Histogram of Gradient (HOG) and the Convolutional Neural Network (CNN) features for character localization. In string localization, a nine-dimension string feature is proposed for discriminative verification after grouping characters. For the final word localization, we learn an optimal splitting strategy based on the interval cues to split strings into words. Experiments on the challenging ICDAR benchmark datasets demonstrate the effectiveness and superiority of our approach.

关键词： Hierarchical framework Character structure feature String feature Convolutional neural network text localization

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text localization in Video Using Multiscale Weber's Local Descriptor

Text Localization in Video Using Multiscale Weber's Local De...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Shekar, B. H. Smitha, M. L. Mangalore Univ Dept Comp Sci Mangalore Karnataka India KVG Coll Engn Dept Master Comp Applicat Sullia Karnataka India

ISBN: (纸本)9781479918232

In this paper, we propose a novel approach for detecting the text present in videos and scene images based on the Multiscale Weber's Local Descriptor (MWLD). Given an input video, the shots are identified and the key frames are extracted based on their spatio-temporal relationship. From each key frame, we detect the local region information using WLD with different radius and neighborhood relationship of pixel values and hence obtained intensity enhanced key frames at multiple scales. These multiscale WLD key frames are merged together and then the horizontal gradients are computed using morphological operations. The obtained results are then binarized and the false positives are eliminated based on geometrical properties. Finally, we employ connected component analysis and morphological dilation operation to determine the text regions that aids in text localization. The experimental results obtained on publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset illustrate that the proposed method can accurately detect and localize texts of various sizes, fonts and colors in videos.

关键词： Key frame Extraction Weber's local descriptor text localization

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