Text Recognition of Low-resolution Document Images

Cheap and versatile cameras make it possible to easily and quickly capture a wide variety of documents. However, low resolution cameras present a challenge to OCR because it is virtually impossible to do character segmentation independently from recognition. In this paper we solve these problems simultaneously by applying methods borrowed from cursive handwriting recognition. To achieve maximum robustness, we use a machine learning approach based on a convolutional neural network. When our system is combined with a language model using dynamic programming, the overall performance is in the vicinity of 80-95% word accuracy on pages captured with a 1024x768 webcam and 10-point text.

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Charles E. Jacobs, Patrice Y. Simard, Paul A. Viola, James Rinker
• Keywords: Convolutional Neural Network, Cursive Handwriting Recognition, Document Analysis, ICDAR 2005, Low Resolution Cameras