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

• [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

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

• [2016-arXiv] Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network paper
• [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

• [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 paper code github

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

• [2014-TPAMI] Word Spotting and Recognition with Embedded Attributes paper homepage code
• [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 paper code
• [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 paper code
• [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 paper code
• [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 paper code

• COCO-Text (Computer Vision Group, Cornell) 2016

  • 63,686 images, 173,589 text instances, 3 fine-grained text attributes.
  • Task: text location and recognition
  • COCO-Text API

• Synthetic Word Dataset (Oxford, VGG) 2014

  • 9 million images covering 90k English words
  • Task: text recognition, segmantation
  • download

• IIIT 5K-Words 2012

  • 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
  • download

• StanfordSynth(Stanford, AI Group) 2012

  • Small single-character images of 62 characters (0-9, a-z, A-Z)
  • Task: text recognition
  • download

• 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 Database 2010

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

• Chars74k 2009

  • 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

Dataset	Discription	Competition Paper
ICDAR 2015	1000 training images and 500 testing images	paper
ICDAR 2013	229 training images and 233 testing images	paper
ICDAR 2011	229 training images and 255 testing images	paper
ICDAR 2005	1001 training images and 489 testing images	paper
ICDAR 2003	181 training images and 251 testing images(word level and character level)	paper

• Scene Text Detection with OpenCV 3
• Handwritten numbers detection and recognition
• Applying OCR Technology for Receipt Recognition
• Convolutional Neural Networks for Object(Car License) Detection
• Extracting text from an image using Ocropus
• Number plate recognition with Tensorflow github
• Using deep learning to break a Captcha system report github
• Breaking reddit captcha with 96% accuracy github