{"id":"src_m9e0vzs78tmz","title":"Gradient-Based Learning Applied to Document Recognition","authorName":"Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner","abstract":"Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters. This paper reviews various methods applied to handwritten character recognition and compares them. Convolutional neural networks are specifically designed to deal with the variability of 2D shapes, and are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling.","tags":["convolutional neural networks","document recognition","lenet","deep learning","handwriting recognition"],"priceUSDC":"0.0001"}