Abstract
This paper presents a system for large vocabulary recognition of on-line handwritten cursive words. The system first uses a filtering module, based on simple letter features, to quickly reduce a large reference dictionary to a smaller number of candidates; the reduced lexicon along with the original input is subsequently fed to a recognition module. In order to exploit the sequential nature of the temporal data, we employ a TDNN-style network architecture which has been successfully used in the speech recognition domain. Explicit segmentation of the input words into characters is avoided by using a sliding window concept where the input word representation (a set of frames) is presented to the neural network-based recognizer sequentially. The outputs of the recognition module are collected and converted into a string of characters that can be matched with the candidate words. A description of the complete system and its components is given.<>