Exploiting forced alignment of time-reversed data for improving HMM-based handwriting segmentation

Abstract

The segmentation of unconstrained handwriting is an important issue for both recognition and synthesis systems. In this direction, hidden Markov model (HMM) has been the most popular method for segmentation of continuous handwriting. It has been employed in both implicit and explicit segmentation-based recognition systems. The main advantage of HMM-based force alignment method is that it can segment the handwriting to basic units (e.g. characters) without any knowledge of ground truth boundaries. However, the alignment acquired from an HMM framework often fails to segment the basic units to their actual boundaries. This paper analyzes the segmentation errors made by the HMM in details and identifies an early character boundary problem. Accordingly, an automatic boundary correction method is proposed which utilizes both the forward and the reverse direction alignments of handwritten data with its transcription. This improves the alignment of data and thus the character boundaries in cursive handwriting. The proposed method enhances the word recognition performance for both the Gaussian mixture model (GMM)-HMM and deep neural network (DNN)-HMM frameworks compared to the conventional approach, when evaluated on UNIPEN ICROW-03 and IAM-onDB databases.