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1. Gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing.

2. Convolutional neural networks are specifically designed to deal with the variability of 2D shapes and outperform all other techniques in handwritten digit recognition tasks.

3. Graph transformer networks (GTN) allow for global training of multimodule systems in real-life document recognition, resulting in improved performance and flexibility. A GTN for reading bank cheques has been deployed commercially and reads several million cheques per day.

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