Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis

Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple “do-it-yourself” implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuni...

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Patrice Simard, David Steinkraus, John C. Platt
• Keywords: Convolutional Neural Network, Document Analysis, ICDAR 2003, Neural Network, Neural Network Methods