Conferences related to Character recognition

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2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Character recognition

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


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Most published Xplore authors for Character recognition

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No authors for "Character recognition"


Xplore Articles related to Character recognition

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Font clustering and classification in document images

2000 10th European Signal Processing Conference, 2000

Clustering and identification of fonts in document images impacts on the performance of optical character recognition (OCR). Therefore font features and their clustering tendency are investigated. Font clustering is implemented both from shape similarity and from OCR performance points of view. A font recognition algorithm is developed to identify the font group with which a given text was created.


Charge transfer imaging

1974 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, 1974

Summary form only given, as follows. The rapidity with which solid-state imaging technology has advanced through-charge transfer techniques places the burden on the product planners to match capability to requirement. As evidenced by the papers to be presented, charge transfer does not mean a single product, but rather a range of products using the same basic technology. It will therefore ...


Ligature based optical character recognition of Urdu- Nastaleeq font

International Multi Topic Conference, 2002. Abstracts. INMIC 2002., 2002

None


Detecting and grouping words in topographic maps by means of perceptual concepts

2000 10th European Signal Processing Conference, 2000

We describe a method for the semi-automated extraction and clustering of characters occurring in scanned topographic maps. The method takes into account the oriented strings that are frequent in topographic maps. We describe the whole application but we emphasize the module devoted to string extraction based on a suitable human vision model related to this particular problem. The experimental results ...


Omnifont Arabic optical character recognition system

Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004., 2004

Optical character recognition systems (OCR) contribute tremendously to the advancement of office automation, provide an easy to use man-machine interface. The solution used in this paper for the problem of Arabic character recognition is insensitive to scale, slight distortions (rotation and misalignment) and limited noise. An image reconstruction is used to segment the connected and overlapping Arabic characters. The program ...


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Educational Resources on Character recognition

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IEEE-USA E-Books

  • Font clustering and classification in document images

    Clustering and identification of fonts in document images impacts on the performance of optical character recognition (OCR). Therefore font features and their clustering tendency are investigated. Font clustering is implemented both from shape similarity and from OCR performance points of view. A font recognition algorithm is developed to identify the font group with which a given text was created.

  • Charge transfer imaging

    Summary form only given, as follows. The rapidity with which solid-state imaging technology has advanced through-charge transfer techniques places the burden on the product planners to match capability to requirement. As evidenced by the papers to be presented, charge transfer does not mean a single product, but rather a range of products using the same basic technology. It will therefore be necessary that the product planner understand the precise capabilities and limitations of the alternatives offered him. Further, he needs to know the advantages the solid-state product can offer in comparison to existing approaches. This is not a one-way responsibility, however, for the device designer must realistically describe his products performance. Parameters that are loosely applied by analogy to image tubes, such as MTF and dynamic range, must be precisely defined. A further consideration is the fact that there are application potentials beyond conventional imaging - optical character recognition, point-of-sale date reading, and target tracking, to mention a few - that have their own particular requirements. Matching the device to the requirement is possible, but requires objective discussion between device designers and product planners.

  • Ligature based optical character recognition of Urdu- Nastaleeq font

    None

  • Detecting and grouping words in topographic maps by means of perceptual concepts

    We describe a method for the semi-automated extraction and clustering of characters occurring in scanned topographic maps. The method takes into account the oriented strings that are frequent in topographic maps. We describe the whole application but we emphasize the module devoted to string extraction based on a suitable human vision model related to this particular problem. The experimental results show the capability to exploit this method for semi-automated character extraction to the aim of building or updating data in Geographic Information Systems.

  • Omnifont Arabic optical character recognition system

    Optical character recognition systems (OCR) contribute tremendously to the advancement of office automation, provide an easy to use man-machine interface. The solution used in this paper for the problem of Arabic character recognition is insensitive to scale, slight distortions (rotation and misalignment) and limited noise. An image reconstruction is used to segment the connected and overlapping Arabic characters. The program also traces tables and column automatically. The system is implemented on PC platform and has a simple and user friendly graphical interface under Windows operating system.

  • An omnifont open-vocabulary OCR system for English and Arabic

    We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus.

  • “Text detection and its segmentation from TV screen”

    This paper presents a system for text extraction on images taken by grabbing the content from the TV screen. The main tendency is the image preparation for the OCR to improve its accuracy. Recognized text is used for automatic generation of TV menu system structure, which is used for verification of TV set operation. This system is used as a part of Black Box Testing system in order to test and functionally verify TV set operation. The improvement is tested on the range of the full image dimensions and the results are compared with those for the original image. The proposed method is robust for different text fonts and styles, and the results show a improvement in character recognition accuracy.

  • Identifing of Alphanumerical Codes in Promotional products by Using of Deep Neural Network

    Using the codes in promotional products was often considered a waste of time. For this reason most codes are not used and promotions do not show sufficient effectiveness. The theme of the project was to identify the promotional code on the product using artificial neural networks and deep learning methods. Bu projede, karakterlerin tanımlanması için, Keras ve Tensorflow kütüphaneleri kullanılarak Convolutional Neural Network (CNN) yapısında bir yapay sinir ağı kullanılmıştır. The project was created in the direction of the Optical Character Recognition application (OCR) requirement that emerged in a software for a customer company in adesso Turkey. The client company's requirement is that a program to recognize characters with a special font. A mobile application has been implemented in the iOS environment to increase the efficiency and ease of implementation of the project. The OCR (Optical Character Recognition) library created in the project has been converted to Objective-C. Then the Objective-C library used in iOS program to use the Python model. In the system where the number of samples used in the training set is 7091, the model accuracy for 1200 test data is 99.7%.

  • Hardware for image alignment by one shear transformation

    This paper presents a hardware for image alignment in an OCR system. This hardware uses a new image-aligning algorithm which performs one shear transformation with no losing pixel information when compared with traditional aligning algorithms. One shear transformation is performed by simply shifting pixels into the target area along a base line without computing new positions of all pixels for aligning. The effectiveness of this algorithm is demonstrated by aligning algorithms. In the hardware, a misaligned image made up of 480 by 380 pixels is aligned within 65 ms.

  • Approximate stroke sequence string matching algorithm for character recognition and analysis

    Given two character images, we would like to measure their similarity or difference. Such a similarity or difference measure facilitates the solution to character recognition and handwriting analysis problems. There is, however, no universal definition for similarity measure satisfying a wide range of characteristics such as the slant, deformation or other invariant constraints. For this reason, we propose a new definition for the character similarity measure. First, the proposed method converts a two-dimensional image into a one-dimensional string. Next, it computes the edit distance by the modified approximate string matching algorithm. We describe how to extract the string information and compute the distance and then present the details of applications in handwriting analysis and both online and offline character recognition.



Standards related to Character recognition

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Jobs related to Character recognition

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