Offline Recognition of Syntax-Constrained Cursive Handwritten Text

The problem of continuous handwritten text (CHT) recognition using standard continuous speech recognition technology is considered. Main advantages of this approach are a) system development is completely based on well understood training techniques and b) no segmentation of sentence or line images into characters or words is required, neither in the training nor in the recognition phases. Many recent papers address this problem in a similar way. Our work aims at contributing to this trend in two main aspects: i) We focus on the recognition of individual, isolated characters using the very same technology as for CHT recognition in order to tune essential representation parameters. The results are themselves interesting since they are comparable with state-of-the-art results on the same standard OCR database. And ii) all the work (except for the image processing and feature extraction steps) is strictly based on a well known and widely available standard toolkit for continuous speech recognition.