Writer-adaptation is the process of converting a writer-independent handwriting recognition system into a writer-dependent system. It can greatly increasing recognition accuracy, given adequate writer models. The limited amount of data a writer provides during training constrains the models' complexity. We show how appropriate use of writer-independent models is important for the adaptation. Our approach uses writer-independent writing style models (lexemes) to identify the styles present in a particular writer's training data. These models are then updated using the writer's data. Lexemes in the writer's data for which an inadequate number of training examples is available are replaced with the writer-independent models. We demonstrate the feasibility of this approach on both isolated handwritten character recognition and unconstrained word recognition tasks. Our results show an average reduction in error rate of 16.3 percent for lowercase characters as compared against representing each of the writer's character classes with a single model. In addition, an average error rate reduction of 9.2 percent is shown on handwritten words using only a small amount of data for adaptation.

Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, directional and shape of the constituent strokes. An online pen computing environment has numerous application in providing an easy human interface for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local online properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We also explore the use of writer dependent models to improve the recognition accuracy.

Writer adaptation is the process of converting a writer-independent handwriting recognition system, which models the characteristics of a large group of writers, into a writer-dependent system, which is tuned for a particular writer. Adaptation has the potential of increasing recognition accuracies, provided adequate models can be constructed for a particular writer. The limited amount of data that a writer typically provides makes the role of writer-independent models crucial in the adaptation process. Our approach to writer-adaptation makes use of writer-independent writing style models (called lexemes), to identify the styles present in a particular writer's training data. These models are then retrained using the writer's data. We demonstrate the feasibility of this approach using hidden Markov models trained on a combination of discretely and cursively written lower case characters. Our results show an average reduction in error rate of 16.3% for lower case characters as compared against representing each of the writer's character classes with a single model.

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 online 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, and experimental results are given for a set of handwritten digits taken from 21 different writers. The use of lexeme representatives reduces classification time by 90.2% while retaining approximately 98% of the recognition accuracy.

We propose a statistical technique of zone finding for the class of documents that art 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.