Convolutional neural network committees for handwritten character classification

Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

446 Scopus citations


In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees. © 2011 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Number of pages5
StatePublished - Dec 2 2011
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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