Context This dataset was created by Yaroslav Bulatov by taking some publicly available fonts and extracting glyphs from them to make a dataset similar to MNIST. There are 10 classes, with letters A-J. Content A set of training and test images of letters from A to J on various typefaces. The images size is 28x28 pixels. Acknowledgements The dataset can be found on Tensorflow github page as well as on the blog from Yaroslav, here. Inspiration This is a pretty good dataset to train classifiers! According to Yaroslav: >> Judging by the examples, one would expect this to be a harder task >> than MNIST. This seems to be the case -- logistic regression on top of >> stacked auto-encoder with fine-tuning gets about 89% accuracy whereas >> same approach gives got 98% on MNIST. Dataset consists of small >> hand-cleaned part, about 19k instances, and large uncleaned dataset, >> 500k instances. Two parts have approximately 0.5% and 6.5% label error >> rate. I got this by looking through glyphs and counting how often my >> guess of the letter didn't match it's unicode value in the font file. Enjoy!