A hidden Markov model (HMM) based continuous speech recognition system is applied to on-line cursive handwriting recognition. The base system is unmodified except for using handwriting feature vectors instead of speech. Due to inherent properties of HMMs, segmentation of the handwritten script sentences is unnecessary. A 1.1% word error rate is achieved for a 3050 word lexicon, 52 character, writer-dependent task and 3%-5% word error rates are obtained for six different writers in a 25,595 word lexicon, 86 character, writer-dependent task. Similarities and differences between the continuous speech and on-line cursive handwriting recognition tasks are explored; the handwriting database collected over the past year is described; and specific implementation details of the handwriting system are discussed. 1. INTRODUCTION Traditionally, the first step in handwriting recognition is the segmentation of words into component characters [1]. However, in modern continuous speech recognition ef...

Abstract—In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style. Index Terms—Style, isogenous patterns, style consistency, style constrained classification, style-bound variant, style-shared variant, Optical Character Recognition, font recognition, field classification, mixture model. 1

Speed, accuracy, and flexibility are crucial to the practical use of on-line handwriting recognition. Besides, extensibility is also an important concern as we move from one domain to another which requires the character set to be extended. In this paper, we will propose a simple yet robust structural approach for recognizing on-line handwriting. Our approach is designed to achieve reasonable speed, fairly high accuracy and sufficient tolerance to variations. At the same time, it maintains a high degree of reusability and hence facilitates extensibility. Experimental results show that the recognition rates are 98.60% for digits, 98.49% for uppercase letters, 97.44% for lowercase letters, and 97.40% for the combined set. When the rejected cases are excluded from the calculation, the rates can be increased to 99.93%, 99.53%, 98.55% and 98.07%, respectively. On the average, the recognition speed is about 7.5 characters per second running in Prolog on a Sun SPARC 10 Unix workstation and the m...

A writer independent handwriting recognition system must be able to recognize a wide variety of handwriting styles, while attempting to obtain a high degree of accuracy when recognizing data from any one of those styles. As the number of writing styles increases, so does the variability of the data's distribution. We then have an optimization problem: how to best model the data, while keeping the representation as simple as possible? If we can identify N different styles of writing individual characters (referred to as lexemes), these can then be modeled as N relatively simple independent distributions. We describe here a template-based system using a string-matching distance measure for the recognition of on-line handwriting which takes advantage of lexemes to reduce the number of templates that must be stored. A method of identifying lexemes and lexeme representatives is shown. Experimental results are given for a set of 600 digits taken from 21 different writers and an...

The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard and recent developments in online handwriting recognition allow for such input modalities. Data entry using a pen forms a natural, convenient interface. The large number of writing styles and the variability between them makes the problem of writer-independent unconstrained handwriting recognition a very challenging pattern recognition problem. The state-of-the-art in online handwriting recognition is such that it has found practical success in very constrained problems. In this thesis, a method of identifying different writing styles, referred to as lexemes, is described. Approaches for constructing both non-parametric and parametric classifiers are described that take advantage of the identified lexemes to f...

The system described in this paper applies Hidden Markov technology to the task of recognizing the handwritten legal amount on personal checks. We argue that the most significant source of error in handwriting recognition is the segmentation process. In traditional handwriting OCR systems, recognition is performed at the character level, using the output of an independent segmentation step. Using a fixed stepsize series of vertical slices from the image, the HMM system described in this paper avoids taking segmentation decisions early in the recognition process. 0 Introduction The current generation of Optical Character Recognition (OCR) systems can be characterized as a pipeline composed of Preprocessing, Segmentation, Classification, and Identification stages. None of these stages are immune to error. Preprocessing may fail to remove existing noise, it may remove portions of the image or add noise by some other mechanism. Segmentation may fail to establish a boundary where there sh...

Contents 1 Background 4 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Types of handwriting recognition systems . . . . . . . . . . . . . . . . 5 1.2.1 Ooe-line recognition . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 On-line recognition . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.3 Character sets . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.4 Writing style variations . . . . . . . . . . . . . . . . . . . . . . 7 1.2.4.1 Variations of characters . . . . . . . . . . . . . . . . 7 1.2.4.2 Alignment of characters . . . . . . . . . . . . . . . . 8 1.2.4.3 Personal background factors . . . . . . . . . . . . . . 8 1.2.4.4 Situational factors . . . . . . . . . . . . . . . . . . . 9 1.2.4.5 Material factors . . . . . . . . . . . . . . . . . . . . . 9 1.2.4.6 Constraints on writing . . . . . . . . . . . . . . . . . 9 1.2.5 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Beneøt

Handwriting recognition has attracted enormous scientific interest because of its potential for improved man/machine interfaces. We have designed an on-line handwritten digit recognition system after the examination of different techniques based on statistical and neural pattern recognition approaches. We collected a digit database from 44 people. We use two representations. The dynamic representation is based on constant length feature vectors of equally distanced points on the pen trajectory. The static representation converts the dynamic information to an image similar to images used in off-line recognition tasks.Then, we tested the well known statistical classification method k-nearest neighbor (k-NN) and neural multi-layer perceptron (MLP) and recurrent networks using both representations. Classifiers trained with dynamic and static representations make misclassifications for different samples. We combine them first by forming a feature vector composed of dynamic and static repr...

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 describes our image retrieval system, which enables the user to search a grey-scale image database intuitively by presenting simple sketches. The database contains 120 different isolated objects (mostly hand tools) with arbitrary orientation. Each of these images is represented by a Hidden Markov Model (HMM) which has been modified in order to obtain rotation and scale invariance. Thus, no labeling of the content or the rotation angle of the object is needed when adding new images to the database. This is particularly important when using query by sketch due to the skew which occurs naturally in human handwriting and in drawings. The retrieved images can be ranked according to the similarity with the query sketch using the output probabilities of the HMMs. Furthermore, the use of HMMs allows efficient pruning and thus a fast retrieval even with large databases. Experiments with our demonstration system showed good retrieval results with several users. 1. Introduction Tradi...