MNIST digits as stroke sequences (code)
Code project to transform the well known MNIST handwritten digit images to sequences of pen strokes, in order to obtain a benchmark data set for sequence learning.
The resulting sequence data set, containing sequences for all 60000 MNIST training images and 10000 test images, is available for download here. See that same page for some examples.
Below, we provide details of how the sequences were constructed. The project consists of two parts:
This is done in C++ by applying incremental thresholding followed by Zhang-Suen thinning.
This part is done in R. Briefly, the procedure does the following:
- To ensure the transformation is consistent, all sequences start from the top left endpoint. If no endpoints exist (this happens for zeroes), the top-left point is used as the starting point.
- A Traveling Salesman Problem (TSP) solver from the TSP package is used to identify an order for visiting the points that minimizes the distance travelled by the pen.
The instructions below describe how the sequences can be generated from the original MNIST images.
This project requires the MNIST handwritten digit data set as input.
Download the files and unpack them into a folder of your choice:
wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz #training set images
wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz #training set labels
wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz #test set images
wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz #test set labels
gzip -d *.gz
Download or fork the code for this project from github. The project contains a submodule, so use --recursive:
git clone --recursive https://github.com/edwin-de-jong/mnist-digits-as-stroke-sequences
This project requires cmake and opencv, here are instructions to install these if needed:
- cmake
sudo apt-get install cmake
sudo apt-get install build-essential
- opencv
sudo apt-get install libopencv-dev
Go to the project folder and compile:
./runcmake #Or edit the paths in the Makefile
make
This should create an executable named createdata.
./createdata << path to your MNIST data folder >>
This will generate thinned images. The results will appear in the subfolder named "digit-images-thinned".
By default, png images will be saved of the results of the first 100 images; this number can be changed by setting maxnrimages in createInputData/createInputData.cpp.
In case you want to modify the code project, you can use cmake. Edit the contents of CMakeLists.txt, and use ./runcmake to generate the updated Makefile.
Open R (e.g. rstudio can be downloaded). Change to the project folder and run the extraction. This requires the TSP package:
install.packages( 'TSP' )
setwd( '~/code/mnist-digits-as-stroke-sequences') #Change this path to your local folder
source( 'R/runExtraction.R' )
This code project was created by Edwin D. de Jong for the purpose of creating a data set for evaluating sequence learning algorithms.
The C++ code for thinning the images makes use of the following existing code projects:
For reading the MNIST images, code provided by Eric Yuan was used; see the readImages subfolder.
For Zhang-Suen thinning, Arnaud Ramey's Voronoi project was used and included as a git submodule in the subfolder named 'voronoi'.
This project is provided under the GNU GPL License version 3.
Please feel free to contact me for any questions or comments. My email is the 3 parts of my name in reverse order:
