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
-
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
-
- 350 high resolution images (average size 1260 ร 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