Venugopal Govindaraju

Offline Arabic handwriting recognition: a survey

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

Analysis of textual images using the Hough transform

The analysis of images of printed pages of text is considered. Since printed text can be viewed as textured line, the use of the Hough transform for detecting straight lines is proposed as an analysis tool. Methods for handling several discretization problems that arise in mapping the rectangular image space to the (ρ, Θ) accumulator array are described. Several applications of analyzing the accumulator array are proposed. They include detecting the text skew angle, determining the signature of a text line so as to accept or reject a block as containing only text, using profile analysis to segment text into lines, and determining whether a textual block is rightside-up or otherwise.

A comparative study on the consistency of features in on-line signature verification

A large number of features have been proposed by researchers for on-line signature verification. However, little work has been done in measuring the consistency and discriminative power of these features. This paper presents a comparative study of features commonly used in on-line signature verification. A consistency model is developed by generalizing the existing feature-based measure to distance-based measure. Experimental results show that the simple features like X-, Y-coordinates, the speed of writing and the angle with the X-axis are amongst the most consistent.

Text - image separation in Devanagari documents

In this paper we present a top-down, projection-profilebased algorithm to separate text blocks from image blocksin a Devanagari document. We use a distinctive feature ofDevanagari text, called Shirorekha (Header Line) to analyzethe pattern produced by Devanagari text in the horizontalprofile. The horizontal profile corresponding to a textblock possesses certain regularity in frequency, orientationand shows spatial cohesion. The algorithm uses these featuresto identify text blocks in a document image containingboth text and graphics.

Creation of data resources and design of an evaluation test bed for Devanagari script recognition

The Indian subcontinent has a large number of languages, dialects, and scripts with the Devanagari script being the primary and most widely used of all the scripts. To date, much of the Devanagari optical character recognition (OCR) research has been restricted to a handful of groups. So, techniques have not yet been widely disseminated or evaluated independently and automated evaluation tools are currently not available for lack of a standard representation of ground-truth and result data. A key reason for the absence of sustained research efforts in off-line Devanagari OCR appears to be the paucity of data resources. Ground truthed data for words and characters, on-line dictionaries, corpora of text documents and reliable, standardized statistical analyses and evaluation tools are currently lacking. So, the creation of such data resources will undoubtedly provide a much needed fillip to researchers working on Devanagari OCR. This paper describes a National Science Foundation sponsored project under the International Digital Libraries program to create data resources that will facilitate development of Devanagari OCR technology and provide a standardized test bed and evaluation tools for Devanagari script recognition.

Active Handwritten Character Recognition Using Genetic Programming

This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources utilized by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.

Real-time Automatic Deceit Detection from Involuntary Facial Expressions

Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.

Generating manifold samples from a handwritten word

Abstract This paper describes a framework for generating variant samples of a handwritten cursive string based on Hollerbachs Oscillation Theory of Handwriting. A single cursive word is analyzed. Significant features of the word are extracted from the velocity profile of the pen movement. These features are used to generate several samples of the same word. The proposed framework for sample generation can be applied only to continuous cursive strings.

Document Analysis Systems for Digital Libraries: Challenges and Opportunities

Implications of technical demands made within digital libraries (DL’s) for document image analysis systems are discussed. The state-of-the-art is summarized, including a digest of themes that emerged during the recent International Workshop on Document Image Analysis for Libraries. We attempt to specify, in considerable detail, the essential features of document analysis systems that can assist in: (a) the creation of DL’s; (b) automatic indexing and retrieval of doc-images within DL’s; (c) the presentation of doc-images to DL users; (d) navigation within and among doc-images in DL’s; and (e) effective use of personal and interactive DL’s.

Information theoretic analysis of postal address fields for automatic address interpretation

This paper concerns a study of information content in postal address fields for automatic address interpretation. Information provided by a combination of address components and information interaction among components is characterized in terms of Shannons entropy. The efficiency of assignment strategies for determining a delivery point code can be compared by the propagation of uncertainty in address components. The quantity of redundancy between components can be computed from the information provided by these components. This information is useful in developing a strategy for selecting a useful component for recovering the value of an uncertain component. The uncertainty of a component based on another known component can be measured by conditional entropy. By ranking the uncertainty quantity, the effective processing flow for determining the value of a candidate component can be constructed.