Neural network positioning and classification of handwritten characters

Abstract

This paper describes two algorithms at the core of the new Kodak Imagelink™ OCR numeric and alphanumeric handprint modules. Both variants of the system were designed to work with fields of characters, typically scanned from forms. The first neural network is trained to find individual characters in the field. Its outputs are associated with an array of pixels in the middle of a sliding window, and they signal the presence of characters centered at corresponding positions. A window containing each detected character (and, possibly, pieces of adjacent characters) is passed on to the second network, which performs the classification. The outputs of both networks are interpreted by an application specific postprocessing module that generates the final label string. Both networks were trained on Gabor projections of the original pixel images, which resulted in higher recognition rates and greater noise immunity. The system has been implemented in specialized parallel hardware, and has been installed and used in production mode at the Driver and Vehicle Licensing Agency (DVLA) in the United Kingdom. The success rate of the purely numeric handprint module (as measured on randomly selected batches of over 200 real forms containing 3500 characters) exceeds 98.5% (character level without rejects), which translates into 93% field rate. After approximately 7% of the characters are rejected, the system achieves a 99.5% character level success rate acceptable for this application. The similarly measured overall success rate of the alphanumeric handprint module exceeds 96% (character level without rejects), which translates into 85% field rate. If approximately 20% of the fields are rejected, the system achieves 99.8% character and 99.5% field success rate.