Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification
Dan Cire\c{s}an, J\"urgen Schmidhuber
TL;DR
This paper introduces multi-column deep neural networks that significantly improve offline handwritten Chinese character recognition, achieving near-human accuracy on ICDAR datasets.
Contribution
The paper presents a novel multi-column deep neural network architecture that sets new benchmarks in Chinese character recognition accuracy.
Findings
Achieved best known recognition rates on ICDAR 2011 and 2013 datasets.
Approached human-level performance in handwritten Chinese character classification.
Demonstrated the effectiveness of multi-column deep learning models for complex character recognition.
Abstract
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.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · Image Retrieval and Classification Techniques