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Unified Networking Lab Images Download



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Unified Networking Lab Images Download

A curated list of resources dedicated to scene text localization and recognition. Any suggestions and pull requests are welcome.

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Papers & Code

Unified Networking Lab Images Download Free

Overview

  • [2015-PAMI] Text Detection and Recognition in Imagery: A Survey paper
  • [2014-Front.Comput.Sci] Scene Text Detection and Recognition: Recent Advances and Future Trends paper
Networking

Visual Geometry Group, University of Oxford

  • [2016-IJCV, M. Jaderberg] Reading Text in the Wild with Convolutional Neural Networks paperdemohomepage
  • [2016-CVPR, A Gupta] Synthetic Data for Text Localisation in Natural Images papercodedata
  • [2015-ICLR, M. Jaderberg] Deep structured output learning for unconstrained text recognition paper
  • [2015-D.Phil Thesis, M. Jaderberg] Deep Learning for Text Spottingpaper
  • [2014-ECCV, M. Jaderberg] Deep Features for Text Spotting papercodemodelGitXiv
  • [2014-NIPS, M. Jaderberg] Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition paperhomepagemodel

CUHK & SIAT

Free and premium stock images of Communications and networking.We have thousands of royalty free stock images for instant download. Nov 3, 2016 - Posts about Unified Networking Labs written by layeredsecurity. NOTE: You need to download and install the different images you want to. Supported images Qemu image namings HowTo's HowTo's Video EVE-NG Pro Cookbook Eve-NG upgrade. - Improved lab loading speed. Multivendor network emulation software that empowers network and security professionals with huge opportunities in the networking world. Clientless management options will allow EVE-NG PRO to be as the best choice.

  • [2016-arXiv] Accurate Text Localization in Natural Image with Cascaded Convolutional Text Networkpaper
  • [2016-AAAI] Reading Scene Text in Deep Convolutional Sequences paper
  • [2016-TIP] Text-Attentional Convolutional Neural Networks for Scene Text Detection paper
  • [2014-ECCV] Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees paper

Downloads EVE-NG Professionnal EVE-NG Community Windows Client Side. Supported images Qemu image namings HowTo's HowTo's Video EVE-NG Pro Cookbook Eve-NG upgrade. Interaction with real network fully supported; simultaneous lab instances. Unetlab has the lowest Google pagerank and bad results in terms of Yandex topical citation index. We found that Unetlab.com is poorly ‘socialized’ in respect to any social network. According to Siteadvisor and Google safe browsing analytics, Unetlab.com is quite a safe domain with no visitor reviews.

Media and Communication Lab, HUST

  • [2016-CVPR] Robust scene text recognition with automatic rectification paper
  • [2016-CVPR] Multi-oriented text detection with fully convolutional networks paper
  • [2015-CoRR] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition papercodegithub

AI Lab, Stanford

Unified Networking Solutions Inc

  • [2012-ICPR, Wang] End-to-End Text Recognition with Convolutional Neural Networks papercodeSVHN Dataset
  • [2012-PhD thesis, David Wu] End-to-End Text Recognition with Convolutional Neural Networks paper

Others

  • [2018-CVPR] FOTS: Fast Oriented Text Spotting With a Unified Network paper
  • [2018-IJCAI] IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection paper
  • [2018-AAAI] PixelLink: Detecting Scene Text via Instance Segmentation papercode
  • [2018-AAAI] SEE: Towards Semi-Supervised End-to-End Scene Text Recognition papercode
  • [2017-arXiv] Fused Text Segmentation Networks for Multi-oriented Scene Text Detection paper
  • [2017-arXiv] WeText: Scene Text Detection under Weak Supervision paper
  • [2017-ICCV] Single Shot Text Detector with Regional Attention paper
  • [2017-ICCV] WordSup: Exploiting Word Annotations for Character based Text Detection paper
  • [2017-arXiv] R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection paper
  • [2017-CVPR] EAST: An Efficient and Accurate Scene Text Detector papercode
  • [2017-arXiv] Cascaded Segmentation-Detection Networks for Word-Level Text Spottingpaper
  • [2017-arXiv] Deep Direct Regression for Multi-Oriented Scene Text Detectionpaper
  • [2017-CVPR] Detecting oriented text in natural images by linking segments papercode
  • [2017-CVPR] Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detectionpaper
  • [2017-arXiv] Arbitrary-Oriented Scene Text Detection via Rotation Proposals paper
  • [2017-AAAI] TextBoxes: A Fast Text Detector with a Single Deep Neural Network papercode
  • [2017-ICCV] Deep TextSpotter: An End-to-End Trainable Scene Text Localization andRecognition Framework papercode
  • [2016-CVPR] Recursive Recurrent Nets with Attention Modeling for OCR in the Wild paper
  • [2016-arXiv] COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images paper
  • [2016-arXiv] DeepText:A Unified Framework for Text Proposal Generation and Text Detection in Natural Images paper
  • [2015 ICDAR] Object Proposals for Text Extraction in the Wild papercode
  • [2014-TPAMI] Word Spotting and Recognition with Embedded Attributes paperhomepagecode

Unified Networking Lab Download

Datasets

Labs
  • MLT 20172017

    • 7200 training, 1800 validation images
    • Bounding box, text transcription, and script annotations
    • Task: text detection, script identification
  • COCO-Text (Computer Vision Group, Cornell)2016

    • 63,686 images, 173,589 text instances, 3 fine-grained text attributes.
    • Task: text location and recognition
  • Synthetic Word Dataset (Oxford, VGG)2014

    • 9 million images covering 90k English words
    • Task: text recognition, segmentation
  • IIIT 5K-Words2012

    • 5000 images from Scene Texts and born-digital (2k training and 3k testing images)
    • Each image is a cropped word image of scene text with case-insensitive labels
    • Task: text recognition
  • StanfordSynth(Stanford, AI Group)2012

    • Small single-character images of 62 characters (0-9, a-z, A-Z)
    • Task: text recognition
  • MSRA Text Detection 500 Database (MSRA-TD500)2012

    • 500 natural images(resolutions of the images vary from 1296x864 to 1920x1280)
    • Chinese, English or mixture of both
    • Task: text detection
  • Street View Text (SVT)2010

    • 350 high resolution images (average size 1260 × 860) (100 images for training and 250 images for testing)
    • Only word level bounding boxes are provided with case-insensitive labels
    • Task: text location
  • KAIST Scene_Text Database2010

    • 3000 images of indoor and outdoor scenes containing text
    • Korean, English (Number), and Mixed (Korean + English + Number)
    • Task: text location, segmantation and recognition
  • Chars74k2009

    • Over 74K images from natural images, as well as a set of synthetically generated characters
    • Small single-character images of 62 characters (0-9, a-z, A-Z)
    • Task: text recognition
  • ICDAR Benchmark Datasets

DatasetDiscriptionCompetition Paper
ICDAR 20151000 training images and 500 testing imagespaper
ICDAR 2013229 training images and 233 testing imagespaper
ICDAR 2011229 training images and 255 testing imagespaper
ICDAR 20051001 training images and 489 testing imagespaper
ICDAR 2003181 training images and 251 testing images(word level and character level)paper

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