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.

Abstract

With the advancement of multimedia, digital video creation has become very common. There is enormous information present in the video, necessitating search techniques for specific content. In this paper, we present an approach that enables search based on the textual information present in the video. Regions of textual information are identified within the frames of the video. Video is then annotated with the textual content present in the images. Traditionally, OCRs are used to extract the text within the video. The choice of OCRs brings in many constraints on the language and the font that they can take care of. We hence propose an approach that enables matching at the image-level and therby avoiding an OCR. Videos containing the query string are retrieved from a video database and sorted based on the relevance. Results are shown from video collections in English,Hindi and Telugu.

Abstract

Performance of hierarchical classifiers depends on two aspects – the performance of the individual classifiers, and the design of the architecture. In this paper, we present a scheme for designing hybrid hierarchical classifiers under user specified constraints on time and space.

Abstract

A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike traditional document segmentation algorithms.This framework is applied to learn the parameters of the document segmentation algorithm using optimization methods like gradient descent and Q-learning.The novelty of our approach lies in learning the segmentation parameters in the absence of ground truth.

Abstract

Searching in scanned documents is an important problem in Digital Libraries. If OCRs are not available, the scanned images are inaccessible. In this paper, we demonstrate a searching procedure without an intermediate textual representation. We achieve effective retrieval from document databases by matching at word-level using image features. Word profiles, structural features and transform domain representations are employed for characterising the word images. A novel partial matching approach based on dynamic time warping (DTW) is proposed to take care of word form variations. With the new partial matching procedure, morphologically variant words become similar in image space. This is specially useful for grouping together similar words for indexing purpose. We extend our formulation for cross-lingual search with the help of transliteration.

Abstract

This paper presents a novel approach for retrieval of relevant documents from a large collection of printed document images, where search in corresponding textual content is practically impossible due to the unavailability of robust OCRs. We achieve effective search by matching at wordlevel using image features. Importance of a document for a given query is also computed at image-level.