HMM-Based On-Line Recognition of Handwritten Whiteboard Notes
Marcus Liwicki, Horst Bunke · Bern Open Repository and Information System (University of Bern) · 2006
In this paper we present an on-line recognition sys-tem for handwritten texts acquired from a whiteboard. This input modality has received relatively little atten-tion in the handwriting recognition community in the past. The system proposed in this paper uses state-of-the-art normalization and feature extraction strategies to trans-form a handwritten text line into a sequence of feature vectors. Additional preprocessing techniques are intro-duced, which significantly increase the word recognition rate. For classification, Hidden Markov Models are used together with a statistical language model. In writer inde-pendent experiments we achieved word recognition rates of 67.3 % on the test set when no language model is used, and 70.8 % by including a language model. 1.