A criterion based on Fourier transform for segmentation of connected digits

Abstract. Segmentation is the most difficult problem in handwritten character recognition systems and often causes major errors in performance. To reach a balance between speed and accuracy, a filter distinguishing connected images from isolated images for multiple stage segmentation is required. The Fourier spectrum is a promising approach to this problem, although it suffers from the heavy influence of stroke width. Therefore, we introduce SFS (SFS) to eliminate the stroke-width effect. Based on the SFS, a set of features and a fine-tuned criterion are presented to classify connected/isolated images. Theoretical analysis demonstrates their soundness, while experimental results demonstrate that this criterion is better than other methods. Received February 18, 2000 / Revised June 3, 2000     

Multiple expert classification: a new methodology for parallel decision fusion

Abstract. A new parallel hybrid decision fusion methodology is proposed. It is demonstrated that existing parallel multiple expert decision combination approaches can be divided into two broad categories based on the implicit decision emphasis implemented. The first category consists of methods implementing computationally intensive decision frameworks incorporating a priori information about the target task domain and the reliability of the participating experts, while the second category encompasses approaches implementing group consensus without assigning any importance to the reliability of the experts and ignoring other contextual information. The methodology proposed in this paper is a hybridisation of these two approaches and has shown significant performance enhancements in terms of higher overall recognition rates along with lower substitution rates. Detailed analysis using two different databases supports this claim. Received January 19, 1999 / Revised March 20, 2000     

Stop word location and identification for adaptive text recognition

Abstract. We propose a new adaptive strategy for text recognition that attempts to derive knowledge about the dominant font on a given page. The strategy uses a linguistic observation that over half of all words in a typical English passage are contained in a small set of less than 150 stop words. A small dictionary of such words is compiled from the Brown corpus. An arbitrary text page first goes through layout analysis that produces word segmentation. A fast procedure is then applied to locate the most likely candidates for those words, using only widths of the word images. The identity of each word is determined using a word shape classifier. Using the word images together with their identities, character prototypes can be extracted using a previously proposed method. We describe experiments using simulated and real images. In an experiment using 400 real page images, we show that on average, eight distinct characters can be learned from each page, and the method is successful on 90% of all the pages. These can serve as useful seeds to bootstrap font learning. Received October 8, 1999 / Revised March 29, 2000     

A generic method of cleaning and enhancing handwritten data from business forms

The automation of business form processing is attracting intensive research interests due to its wide application and its reduction of the heavy workload due to manual processing. Preparing clean and clear images for the recognition engines is often taken for granted as a trivial task that requires little attention. In reality, handwritten data usually touch or cross the preprinted form frames and texts, creating tremendous problems for the recognition engines. In this paper, we contribute answers to two questions: “Why do we need cleaning and enhancement procedures in form processing systems?” and “How can we clean and enhance the hand-filled items with easy implementation and high processing speed?” Here, we propose a generic system including only cleaning and enhancing phases. In the cleaning phase, the system registers a template to the input form by aligning corresponding landmarks. A unified morphological scheme is proposed to remove the form frames and restore the broken handwriting from gray or binary images. When the handwriting is found touching or crossing preprinted texts, morphological operations based on statistical features are used to clean it. In applications where a black-and-white scanning mode is adopted, handwriting may contain broken or hollow strokes due to improper thresholding parameters. Therefore, we have designed a module to enhance the image quality based on morphological operations. Subjective and objective evaluations have been studied to show the effectiveness of the proposed procedures. Received January 19, 2000 / Revised March 20, 2001     

Maximum mutual information training for an online neural predictive handwritten word recognition system

In this paper, we present a hybrid online handwriting recognition system based on hidden Markov models (HMMs). It is devoted to word recognition using large vocabularies. An adaptive segmentation of words into letters is integrated with recognition, and is at the heart of the training phase. A word-model is a left-right HMM in which each state is a predictive multilayer perceptron that performs local regression on the drawing (i.e., the written word) relying on a context of observations. A discriminative training paradigm related to maximum mutual information is used, and its potential is shown on a database of 9,781 words. Received June 19, 2000 / Revised October 16, 2000     

Creating word-level language models for large-vocabulary handwriting recognition

We discuss development of a word-unigram language model for online handwriting recognition. First, we tokenize a text corpus into words, contrasting with tokenization methods designed for other purposes. Second, we select for our model a subset of the words found, discussing deviations from an N-most-frequent-words approach. From a 600-million-word corpus, we generated a 53,000-word model which eliminates 45% of word-recognition errors made by a character-level-model baseline system. We anticipate that our methods will be applicable to offline recognition as well, and to some extent to other recognizers, such as speech recognizers and video retrieval systems. Received: November 1, 2001 / Revised version: July 22, 2002     

Asynchronous group mutual exclusion

Abstract. Mutual exclusion and concurrency are two fundamental and essentially opposite features in distributed systems. However, in some applications such as Computer Supported Cooperative Work (CSCW) we have found it necessary to impose mutual exclusion on different groups of processes in accessing a resource, while allowing processes of the same group to share the resource. To our knowledge, no such design issue has been previously raised in the literature. In this paper we address this issue by presenting a new problem, called Congenial Talking Philosophers, to model group mutual exclusion. We also propose several criteria to evaluate solutions of the problem and to measure their performance. Finally, we provide an efficient and highly concurrent distributed algorithm for the problem in a shared-memory model where processes communicate by reading from and writing to shared variables. The distributed algorithm meets the proposed criteria, and has performance similar to some naive but centralized solutions to the problem. Received: November 1998 / Accepted: April 2000     

Fractal analysis of tumor in brain MR images

Abstract. The purpose of this study is to discuss existing fractal-based algorithms and propose novel improvements of these algorithms to identify tumors in brain magnetic-response (MR) images. Considerable research has been pursued on fractal geometry in various aspects of image analysis and pattern recognition. Magnetic-resonance images typically have a degree of noise and randomness associated with the natural random nature of structure. Thus, fractal analysis is appropriate for MR image analysis. For tumor detection, we describe existing fractal-based techniques and propose three modified algorithms using fractal analysis models. For each new method, the brain MR images are divided into a number of pieces. The first method involves thresholding the pixel intensity values; hence, we call the technique piecewise-threshold-box-counting (PTBC) method. For the subsequent methods, the intensity is treated as the third dimension. We implement the improved piecewise-modified-box-counting (PMBC) and piecewise-triangular-prism-surface-area (PTPSA) methods, respectively. With the PTBC method, we find the differences in intensity histogram and fractal dimension between normal and tumor images. Using the PMBC and PTPSA methods, we may detect and locate the tumor in the brain MR images more accurately. Thus, the novel techniques proposed herein offer satisfactory tumor identification. Received: 13 October 2001 / Accepted: 28 May 2002 Correspondence to: K.M. Iftekharuddin     

Evaluating the performance of table processing algorithms

While techniques for evaluating the performance of lower-level document analysis tasks such as optical character recognition have gained acceptance in the literature, attempts to formalize the problem for higher-level algorithms, while receiving a fair amount of attention in terms of theory, have generally been less successful in practice, perhaps owing to their complexity. In this paper, we introduce intuitive, easy-to-implement evaluation schemes for the related problems of table detection and table structure recognition. We also present the results of several small experiments, demonstrating how well the methodologies work and the useful sorts of feedback they provide. We first consider the table detection problem. Here algorithms can yield various classes of errors, including non-table regions improperly labeled as tables (insertion errors), tables missed completely (deletion errors), larger tables broken into a number of smaller ones (splitting errors), and groups of smaller tables combined to form larger ones (merging errors). This leads naturally to the use of an edit distance approach for assessing the results of table detection. Next we address the problem of evaluating table structure recognition. Our model is based on a directed acyclic attribute graph, or table DAG. We describe a new paradigm, “graph probing,” for comparing the results returned by the recognition system and the representation created during ground-truthing. Probing is in fact a general concept that could be applied to other document recognition tasks as well. Received July 18, 2000 / Accepted October 4, 2001