this paper applies Hidden Markov technology both to the task of recognizing the cursive legal amount on personal checks and the isolated (numeric) courtesy amount. Throughout the paper, our primary goal is to present methods that will allow the engineer to gain maximum leverage from a limited amount of training data. 1 Introduction

It is almost universally accepted in speech recognition that phone- or word-level segmentation prior to recognition is neither feasible nor desirable, and in the dynamic (pen-based) handwriting recognition domain the success of segmentation-free techniques points to the same conclusion. But in image-based handwriting recognition, this conclusion is far from being firmly established, and the results presented in this paper show that systems employing character-level presegmentation can be more effective, even within the same HMM paradigm, than systems relying on sliding window feature extraction. We describe two variants of a Hidden Markov system recognizing handwritten addresses on US mail, one with presegmentation and one without, and report results on the CEDAR data set. 1. INTRODUCTION Any approach to speech and handwriting recognition must take into account that the signal is composed from a succession of alphabetic units (phonemes or graphemes). In the early work on speech recog...

Due to the difficulty of character segmentation in cursive handwriting recognition, much recent research has turned to segmentation free approaches of word recognition. While techniques of feature extraction for presegmented characters have been thoroughly explored in the literature, an evaluation of features for use with segmentation during recognition techniques remains sparse. The main purpose of this thesis is to provide a comparison of a number of feature extraction techniques applied to the domain of legal amount recognition in bank checks. An experimental system using Hidden Markov Models and a horizontally sliding window is described. Results are presented for the recognition of the entire legal field using a variety of features. Of the experiments presented here, the best results were obtained by concatenating the feature vectors from the present, previous, and next window...

this paper we present an HMM based

We propose a statistical technique of zone finding for the class of documents that are neither rigidly structured like tax forms nor very unstructured like magazine pages or engineering drawings. Given an initial window assumed to contain the final zone (bounding box) of interest, and a `signature ' of the target, we propose to locate the final zone by a combination of simple outside in and inside out searches based on the assumption that the coordinates of the target have unimodal distribution. Results are presented in the bank check domain, and the applicability of the technique to other domains is discussed. 0. Introduction Proc 13th ICPR, Vienna (1996) Vol III, pp 818-822 Real world Optical Character Recognition (OCR) systems rarely enjoy the luxury, often taken for granted in more academic systems, of working with clearly delineated text zones. In fact, the task of zoning, or region extraction, i.e. identifying and precisely demarcating the zone(s) containing the text to be recog...

Abstract-Research on recognizing Bengali script has been started since mid 1980’s. A variety of different techniques have been applied and the performance is examined. In this paper we present a high performance domain specific OCR for recognizing Bengali script. We select the training data set from the script of the specified domain. We choose Hidden Markov Model (HMM) for character classification due to its simple and straightforward way of representation. We examine the primary error types that mainly occurred at preprocessing level and carefully handled those errors by adding special error correcting module as a part of recognizer. Finally we added a dictionary and some error specific rules to correct the probable errors after the word formation is done. The entire technique significantly increases the performance of the OCR for a specific domain to a great extent. I.