LeNet-5: Convolutional Networks for Character Recognition

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.

There are no comments yet.

Please log in to write the first comment.