On-line handwriting recognition systems get the information how a symbol is written. In contrast, OCR only gets the pixel map.
I've created a system that can be used to work with handwriting recognition systems in my bachelor's thesis.
The website write-math.com was used to collect data. The source is at github.com/MartinThoma/write-math.
hwrt toolkit was created to
work with on-line handwritten symbols. The toolkit is documented at
The raw data can be downloaded with this toolkit.
The toolkit can be used to classify data on your computer (without internet connection):
nntoolkit was created to
have a free software to create, train, test and evaluate neural networks.
All experiments configuration files are saved in the project github.com/MartinThoma/hwr-experiments.
The data can be downloaded from write-math.com/data. I will try to keep a relatively recent version online. You can contact me if you want the latest version. However, I should note that currently (2015-04-12) this is about 3.7GB. This means sharing the data is not that easy.
- 06.11.2014: Final presentation for bachelor's thesis
- 07.11.2014: My bachelor's thesis. I've got the best grade (1.0) for it ☺. Please note that the submission to arxiv was later and a couple of typos were fixed as well as the term "data multiplication" was replaced by "data augmentation".
- 29.06.2015: An updated, condensed version of my bachelor's thesis.
- What I called "data multiplication" is called "data augmentation" by others (e.g. ImageNet Classification with Deep Convolutional Neural Networks, Deep Image: Scaling up Image Recognition, Classifying plankton with deep neural networks)