Despite the success of many pattern recognition systems in constrained domains, problems that involve noisy input and many classes remain difficult. A promising direction is to use several classifiers simultaneously, such that they can complement each other in correctness. This thesis is concerned with decision combination in a multiple classifier system that is critical to its success. A multiple classifier system consists of a set of classifiers and a decision combination function. It is a preferred solution to a complex recognition problem because it allows simultaneous use of feature descriptors of many types, corresponding measures of similarity, and many classification procedures. It also allows dynamic selection, so that classifiers adapted to inputs of a particular type may be applied only when those inputs are encountered. Decisions by the classifiers are represented as rankings of the class set that are derivable from the results of feature matching. Rank scores contain more ...

This paper describes techniques to separate a line of unconstrained (written in a natural manner) handwritten text into words. When the writing style is unconstrained, recognition of individual components may be unreliable so they must be grouped together into word hypotheses, before recognition algorithms (which may require dictionaries) can be used. Our system uses original algorithms to determine distances between components in a text line and to detect punctuation. The algorithms are tested on nearly 3000 handwritten text lines extracted from postal address blocks. We give a detailed performance analysis of the complete system and its components. Key Words: text understanding , pattern recognition , handwritten text recognition 1. INTRODUCTION This research focuses on separating a line of handwritten text into words by determining the location of inter-word gaps (gaps between words). This paper describes and evaluates distance measurement algorithms, punctuation detection algorith...

Recognition of degraded text is a challenging problem. To improve the performance of an OCR system on degraded images of text, postprocessing techniques are critical. The objective of postprocessing is to correct errors or to resolve ambiguities in OCR results by using contextual information. Depending on the extent of context used, there are different levels of postprocessing. In current commercial OCR systems, word-level postprocessing methods, such as dictionary-lookup, have been applied successfully. However, many OCR errors cannot be corrected by word-level postprocessing. To overcome this limitation, passage-level postprocessing, in which global contextual information is utilized, is necessary. In most current studies on passage-level postprocessing, linguistic context is the major resource to be exploited. This thesis addresses problems in degraded text recognition and discusses potential solutions through passage-level postprocessing. The objective is to develop a postprocessin...

A document image is a visual representation of a printed page such as a journal article page, a facsimile cover page, a technical document, an o#ce letter, etc. Document understanding as a research endeavor consists of studying all processes involved in taking a document through various representations: from a scanned physical document to high-level semantic descriptions of the document. Some of the types of representation that are useful are: editable descriptions, descriptions that enable exact reproductions and high-level semantic descriptions about document content. This report is a de#nition of #ve research subdomains within document understanding as pertaining to predominantly printed documents. The topics described are: modular architectures for document understanding; decomposition and structural analysis of documents; model-based OCR; table, diagram and image understanding; and performance evaluation under distortion and noise. 1 Each of the main sections of this paper were ...

This paper presents a new perception-based model for reading cursive script. We limit the scope of our study to the off-line recognition of isolated cursive words. Starting from observations and assumptions used in the elaboration of reading models, we describe the organization of our pseudoneuronal system and show the role of the activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics specific to cursive script. First, we use appropriate features such as ascenders, descenders and loops. Second, we deal with the ambiguity of letter location by introducing the concept of fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base.

This paper presents a new perception-based model for reading cursive script. We limit the scope of our study to the off-line recognition of isolated cursive words. Starting from observations and assumptions used in the elaboration of reading models, we describe the organization of our pseudoneuronal system and show the role of the activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics specific to cursive script. First, we use appropriate features such as ascenders, descenders and loops. Second, we deal with the ambiguity of letter location by introducing the concept of fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base. 1 Introduction Teaching t...

We use Markov random fields to improve the output of the thinned Sobel edge detector, applied to images compressed using the JPEG technique. JPEG compression saves a lot of file space, however it introduces correlated errors into the images. This is exactly a circumstance for which our recently developed double neighborhood MRFs are suited (Milun and Sher, 1992a). Double neighborhood MRFs are constructed by sampling from pairs of original images together with noisy imagery. Thus we create a probability density function for pairs of neighborhoods across both images. This models the noise within the MRF probability density function without having to make assumptions about its form. This provides an easy way to generate Markov random fields for annealing or other relaxation methods. We train the double neighborhood MRF on true edge-maps and edge-maps generated as output of a Sobel edge detector (Duda and Hart, 1973) on compressed images. Our method improves the generated edge-maps as veri...

Recognition of text by whole word shapes generates a set of candidate words for each printed word. A Hidden Markov Model (HMM) of syntax may be used to find the most probable sequence of syntactic tags for a sentence given the sequence of candidate sets. Candidate sets are then reduced by removing all words which are not associated with the chosen tag. We show that the tagging performance of the HMM does not deteriorate despite an increasing proportion of mis-classified words. We also show that using the model significantly reduces the number of candidates. 1

Abstract not found

We present an empirical investigation of the importance of accurate assessment of prior probabilities in a typical visual classification problem, handwritten ZIP Code recognition. We investigated prior probabilities for individual digits and entire zipcodes; the results for priors of individual digits are summarized here and discussed in detail in [l]. In our studies of prior distributions over entire ZIP Code we found that qualitative information had a major effect on the efficacy of the algorithm while quantitative information is relatively unimportant. 1