This paper outlines the advancements in Optical Character Recognition (OCR), particularly focusing on handwritten character and word recognition using Neural Networks. The authors, affiliated with AT&T Labs-Research, detail various machine learning techniques, including Gradient-Based Learning and Convolutional Neural Networks (CNNs) like LeNet-5, highlighting their effectiveness in handling high-dimensional inputs and generating intricate decision functions. A significant portion of the paper is dedicated to Graph Transformer Networks (GTNs), a multi-module system designed to interpret sequences of characters by leveraging graph-based representations and global training methods to reduce errors. The paper also describes the creation and use of the MNIST dataset, a benchmark for handwritten digit recognition, and discusses the practical application of these technologies in commercial check reading systems and online handwriting recognition.

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