Overview

This is a synthetically generated dataset, in which word instances are placed in natural scene images, while taking into account the scene layout.

The dataset consists of 800 thousand images with approximately 8 million synthetic word instances. Each text instance is annotated with its text-string, word-level and character-level bounding-boxes.

Data Format

SynthText in the Wild Dataset

Ankush Gupta, Andrea Vedaldi, and Andrew Zisserman Visual Geometry Group, University of Oxford, 2016

Data format:

SynthText.zip (size = 42074172 bytes (41GB)) contains 858,750 synthetic scene-image files (.jpg) split into 200 directories, with 7,266,866 word-instances, and 28,971,487 characters.

Ground-truth annotations are contained in the file "gt.mat" (Matlab format). The file "gt.mat" contains the following cell-arrays, each of size 1x858750:

1. imnames : names of the image files

2. wordBB : word-level bounding-boxes for each image, represented by tensors of size 2x4xNWORDS_i, where:

   • the first dimension is 2 for x and y respectively,
   • the second dimension corresponds to the 4 points (clockwise, starting from top-left), and
   • the third dimension of size NWORDS_i, corresponds to the number of words in the i_th image.

3. charBB : character-level bounding-boxes, each represented by a tensor of size 2x4xNCHARS_i (format is same as wordBB's above)

4. txt : text-strings contained in each image (char array).

             Words which belong to the same "instance", i.e.,
             those rendered in the same region with the same font, color,
             distortion etc., are grouped together; the instance
             boundaries are demarcated by the line-feed character (ASCII: 10)

             A "word" is any contiguous substring of non-whitespace
             characters.

             A "character" is defined as any non-whitespace character.

For any questions or comments, contact Ankush Gupta at: removethisifyouarehuman-ankush@robots.ox.ac.uk

Citation

If you use this data, please cite:

@InProceedings{Gupta16,
  author       = "Ankush Gupta and Andrea Vedaldi and Andrew Zisserman",
  title        = "Synthetic Data for Text Localisation in Natural Images",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  year         = "2016",
}