Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification

By Dan Claudiu Ciresan, J. Urgen Schmidhuber. 2013

188 citations recorded by the source corpus.

Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “A new record on the ICDAR Chinese handwriting recognition benchmark (over 3700 classes) was set on a desktop machine by an ensemble of GPU-MPCNNs (Section 5.19) with almost human performance (Ciresan & Schmidhuber, 2013); compare (Yin,Wang, Zhang, & Liu, 2013).”
Self-Normalizing Neural Networks (2017, background): “RNNs have won handwriting recognition challenges (Chinese and Arabic handwriting) [33, 13, 6] and Kaggle challenges, such as the “Grasp-and Lift EEG” competition.”
My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 (2013, methodology): “A new record [24] on the ICDAR Chinese handwriting recognition benchmark (over 3700 classes) was set on a desktop machine by a GPU-MCMPCNN with almost human performance.”
Drawing and Recognizing Chinese Characters with Recurrent Neural Network (2016, background): “to the task of offline recognition [9], [10], [11], [12].”