Project Reading 1: Online handwriting recognition: the NPen++ recognizer

Citation

Jaeger, Stefan, et al. "Online handwriting recognition: the NPen++ recognizer."International Journal on Document Analysis and Recognition 3.3 (2001): 169-180.

PDF

Summary

The paper presents an on line handwriting recognition system called NPen++. This recognition engine is based on multi state time delay neural networks. The recognition accuracy was found to be from 96 percent for a dictionary of size 5000 and around 93 percent for a dictionary of around 20000 words. The preprocessing state has various steps from normalizing size, normalizing rotations, interpolating missing points, smoothing, normalizing inclination, resampling and removing delayed strokes.

Discussion

The features computed for recognition are writing direction, curvature, pen-up/pen-down times, hat feature, aspect, curliness, line-ness, slope, ascenders/descenders, context bitmaps. The Multi-State Time Delay Neural Networks (MS-TDNN). The system was evaluated on many datasets from UKA, CMU and MIT which included both printed and cursive writings.