Writer-Dependent Recognition of Handwritten Whiteboard Notes in Smart Meeting Room Environments

Abstract

In this paper we present a writer-dependent handwriting recognition system based on hidden Markov models (HMMs). This system, which has been developed in the context of research on smart meeting rooms, operates in two stages. First, a Gaussian mixture model (GMM)-based writer identification system developed for smart meeting rooms identifies the person writing on the whiteboard. Then a recognition system adapted to the individual writer is applied. Two different methods for obtaining writer-dependent recognizers are proposed. The first method uses the available writer-specific data to train an individual recognition system for each writer from scratch, while the second method takes a writer-independent recognizer and adapts it with the data from the considered writer. The experiments have been performed on the IAM-OnDB. In the first stage,the writer identification system produces a perfect identification rate. In the second stage, the writer-specific recognition system gets significantly better recognition results, compared to the writer-independent recognizer. The final word recognition rate on the IAM-OnDB-t1 benchmark task is close to 80 %.