Abstract

Many problems in computer vision such as pose recovery and structure estimation are formulated as a minimization process. These problems vary in the use of image measurements directly or using them to extract 3D cues in the minimization process. Hybrid methods have the advantage of combining the 2D and 3D visual information to improve the performance over the above two methods. In this paper, we present a new formulation for minimizing a class of hybrid error functions. This is done by using 2D information from the image space and 3D information from the Cartesian space in one error function. Applications to visual servoing and image alignment problems are presented. The positioning task of a robot arm has been formulated as a minimization problem. Gradient decent as a first order approxima ion and Gauss-Newton as a second order approximation are considered in this paper. Simulation results show, comparing with 2 1/2 D hybrid method, that these two\ methods provide an efficient solution to the features visibility problems and the camera trajectory in the Cartesian space.

Abstract

Most of the state-of-the-art segmentation algorithms are de-signed to handle complex document layouts and backgrounds, while assuming a simple script structure such as in Roman script. They perform poorly when used with Indian languages, where the components are not strictly collinear. In this paper, we propose a document segmentation algorithm that can handle the complexity of Indian scripts in large document image collections. Segmentation is posed as a graph cut problem that incorporates the a prior information from script structure in the objective function of the cut. We show that this information can be learned automatically and be adapted within a collection of documents (a book)and across collections to achieve accurate segmentation. We show the results on Indian language documents in Telugu script. The approach is also applicable to other languages with complex scripts such as Bangla,Kannada, Malayalam, and Urdu

Abstract

Annotated datasets of handwriting are a prerequisite to attempt a variety of problems such as building recognizers, developing writer identification algorithms, etc. However, the annotation of large datasets is a tedious and expensive process, especially at the character or stroke level. In this paper we propose a novel, automated method for annotation at the character level, given a parallel corpus of online handwritten data and the corresponding text. The method employs a model-based handwriting synthesis unit to map the two corpora to the same space and the annotation is propagated to the word level and then to the individual characters using elastic matching. The initial results of annotation are used to improve the handwriting synthesis model for the user under consideration, which in turn refines the annotation. The method can take care of errors in the handwriting such as spurious and missing strokes or characters. The output is stored in the UPXInkML format

Abstract

Digital Libraries have received wide attention in the recent years allowing access to digital information from anywhere across the world. They have become widely accepted and even preferred information sources in areas of education, science and others. When the full potential of digital libraries is realized, any citizen will for the first time be able to access all human knowledge immediately from any location. For a successful and useful Digital Library, the assurance of quality of the product from digitization process is quite essential. In this paper we discuss the major quality concerns of data from the digitization process, and how we assure for better quality before they are web enabled for the end-user to use. Also we discuss a Quality Management Framework that we applied for the improvement of both the Quality of the digital output and the efficiency of the processes.

Abstract

Many of the human activities such as Jumping, Squatting have a correlated spatiotemporal structure. They are composed of homogeneous units. These units, which we refer to as actions, are often common to more than one activity. Therefore, it is essential to have a representation which can capture these activities effectively. To develop this, we model the frames of activities as a mixture model of actions and employ a probabilistic approach to learn their low-dimensional representation. We present recognition results on seven activities performed by various individuals. The results demonstrate the versatility and the ability of the model to capture the ensemble of human activities.

Abstract

In this paper we propose a novel technique for robust visual servoing in presence of a large proportion of outliers in image measurements. The method employs robust statistical techniques and novel view prediction for improving the performance. We identify a set of points from initial and reference images and compute the essential matrix relating them. The selected points are predicted from the initial and reference images to the current frame using essential matrices. A function of the difference between the observed and predicted image point measurements is used to identify outliers. This technique is validated with many experiments and compared with other robust methods in a simulation framework.

Abstract

In this paper we describe a spectrum of algorithm for rectification of document images of camera based analysis and recognition. Clues like document boundaries page ,page layout information, organisation of text and graphics, components, apriori knowledge of script or selected symbols etc. are effectively used for removal of perspective effect and comforting the frontal view needed for a typical document image analysis algorithm. Appropriate results from projective geometry for planar surfaces are exploited in these situations.

Abstract

In Africa, there are a number of languages with their own indigenous scripts. This paper presents an OCR for Amharic scripts. Amharic is the official and working language of Ethiopia. This is possibly the first attempt towards the development of an OCR system for Amharic. Research in the recognition of Amharic script faces major challenges due to (i) the use of more than 300 characters in writing and (ii) existence of a large set of visually similar characters. In this paper, we propose a two-stage feature extraction scheme using PCA and LDA, followed by a decision DAG classifier with SVMs as the nodes. Recognition results are presented to demonstrate the performance on the various printing variations (fonts, styles and sizes) and real-life degraded documents such as books, magazines and newspapers.

Abstract

A discriminant-based framework for automatic recognition of online handwriting data is presented in this paper. We identify the substrokes that are more useful in discriminating between two online strokes. A similarity/dissimilarity score is computed based on the discriminatory potential of various parts of the stroke for the classification task. The discriminatory potential is then converted to the relative importance of the substroke. Experimental verification on online data such as numerals, characters supports our claims. We achieve an average reduction of
 in the classification error rate on many test sets of similar character pair

Abstract

In this paper we describe a prototype system that recognizes text in images, captured by camera phones and provide access to the content in a different language. Two Indian languages – Hindi and Tamil are used to demonstrate such a system. The prototype system is built using off-the-shelf components, and in house developed algorithms. The acquired image is first transferred to a server, which corrects the perspective distortions, detects recognizes and then translates the text (word). This translated text is sent back to the camera-phone in a suitable form. We have also described here, the Hindi and Tamil OCRs which we use for the character recognition. We also propose methods to make the recognizer efficient in storage and computation. The translated text, along with any additional information, is transmitted back to the user.