The Activity Metric for Low Resource, On-line Character Recognition

Abstract

This work presents an algorithm for on-line character recognition that is fast, portable, and consumes very little memory for code or data. The algorithm is alphabet-independent, and does not require training beyond entering the alphabet once. This algorithm uses a novel, parameter-based method of feature extraction, \textit{activity}, to achieve high recognition accuracy. Recognition accuracy is shown to be improvable dynamically without further input from the user. The algorithm brings the capability to do character recognition to classes of devices that heretofore have not possessed that capability because of limited computing resources, including mobile handsets, PDAs, pagers, toys, and other small devices. It achieves recognition speeds of 16.8 characters per second on a 20MHz, 8-bit microcontroller without floating-point. The alphabet-independent nature of the algorithm combined with its inherent resistance to regular noise interference may allow it to enhance the capability of persons with impaired motor or nervous systems to communicate with devices by writing or gesturing commands. Additionally, two human studies demonstrate the effectiveness of a simple, activity-based recognizer for users of the stylized Graffiti alphabet and for non-stylized variants of the English alphabet. A final experiment shows how recognition accuracy can be improved per user by modifying the parameters of the activity metric over samples collected in the non-stylized study.