Coding and comparison of DAG's as a novel neural structure with applications to on-line handwriting recognition

This paper applies directed acyclic graphs (DAGs) to a large class of (temporal) pattern recognition problems and other recognition problems where the data has a linear ordering. The data streams are coded (DAG-coded) into DAGs for robust segmentation. The similarity of two streams can be manifested as the path matching score of the two corresponding DAGs. This paper also presents an efficient and robust dynamic programming algorithm for their comparisons (DAG-compare). Since the DAG-coding methodology directly provides a robust segmentation process, it can be applied recursively to create a novel system architecture. The DAG structure also allows adaptive restructuring, leading to a novel approach to neural information processing. By using these elementary operations on DAGs, we can recognize on average 94.0% (writer-dependent) of the isolated handwritten cursive characters. DAG-coding may also be applied to speech recognition or any other continuous streams where a robust multipath segmentation aids the recognition process.