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This paper describes the derivation of probability of correctness from scores assigned by most recognizers. Derivation of probability values puts the output of different recognizers on the same scale; this makes comparison across recognizers trivial. Index Terms - Recognizer, reestimation methods, feature space, classifier, probability.

A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi. Index Terms - Handwritten word recognition, segmentation algorithm, variable duration, chain code representation, dynamic programming.

Normalization of slant and skew is often used in processing a word image before recognition. In this paper, we prove the theoretical equivalence of different methods for slant and skew corrections. In particular, we show that correcting first for skew by rotation and then for slant by a shear transformation in the horizontal direction is equivalent to first correcting for slant by a shear transformation in the horizontal direction and then for skew by a shear transformation in the vertical direction. Our proof can be easily modified to prove equivalence of other methods for correcting the slant and skew. Index Terms--Image preprocessing, slant normalization, skew normalization, handwriting recognition.

The Holistic paradigm in handwritten word recognition treats the word as a single, indivisible entity and attempts to recognize words from their overall shape, as opposed to their character contents. In this survey, we have attempted to take a fresh look at the potential role of the Holistic paradigm in handwritten word recognition. The survey begins with an overview of studies of reading which provide evidence for the existence of a parallel holistic reading process in both developing and skilled readers. In what we believe is a fresh perspective on handwriting recognition, approaches to recognition are characterized as forming a continuous spectrum based on the visual complexity of the unit of recognition employed and an attempt is made to interpret well-known paradigms of word recognition in this framework. An overview of features, methodologies, representations, and matching techniques employed by holistic approaches is presented. Index Terms--Handwriting recognition, holistic paradigms, analytical methods, reading theory, pattern recognition.

This paper describes a character recognition methodology (henceforth referred to as Hierarchical OCR) that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The Hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain "zones" of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the Hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times faster than a neural network method. The comparsion uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the Hierarchical OCR. This is at par with the other two traditional methods. Index Terms--Pattern recognition, character/digit recognition, multiresolution, feature space, hierarchical classification, recursion.