Catálogo de publicaciones - libros

This paper concerns automatic recognition of both printed and handwritten Bangla numerals. Such mixed numerals may appear in documents like application forms, postal mail, bank checks etc. Some pixel-based and shape-based features are chosen for the purpose of recognition. The pixel-based features are normalized pixel density over 4 X 4 blocks in which the numeral bounding-box is partitioned. The shape-based features are normalized position of holes, end-points, intersections and radius of curvature of strokes found in each block. A multi-layer neural network architecture was chosen as classifier of the mixed class of handwritten and printed numerals. For the mixture of twenty three different fonts of printed numerals of various sizes and 10,500 handwritten numerals, an overall recognition accuracy of 97.2% has been achieved.

Recognition of handwritten characters is a challenging task because of the variability involved in the writing styles of different individuals. In this paper we propose a quadratic classifier based scheme for the recognition of off-line Devnagari handwritten characters. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Based on the chain code histogram, here we have used 64 dimensional features for recognition. These chain code features are fed to the quadratic classifier for recognition. From the proposed scheme we obtained 98.86% and 80.36% recognition accuracy on Devnagari numerals and characters, respectively. We used five-fold cross-validation technique for result computation.

Recently, a few works on recognition of handwritten Bangla characters have been reported in the literature. However, there is scope for further research in this area. In the present article, results of our recent study on recognition of handwritten Bangla basic characters will be reported. This is a 50 class problem since the alphabet of Bangla has 50 basic characters. In this study, features are obtained by computing local chain code histograms of input character shape. Comparative recognition results are obtained between computation of the above feature based on the contour and one-pixel skeletal representations of the input character image. Also, the classification results are obtained after down sampling the histogram feature by applying Gaussian filter in both these cases. Multilayer perceptrons (MLP) trained by backpropagation (BP) algorithm are used as classifiers in the present study. Near exhaustive studies are done for selection of its hidden layer size. An analysis of the misclassified samples shows an interesting error pattern and this has been used for further improvement in the recognition results. Final recognition accuracies on the training and the test sets are respectively 94.65% and 92.14%.

In this work, we present a recently developed evaluation framework for video OCR specifically for English Text but could well be generalized for other languages as well. Earlier works include the development of an evaluation strategy for text detection and tracking in video, this work is a natural extension. We sucessfully port and use the ASR metrics used in the speech community here in the video domain. Further, we also show results on a small pilot corpus which involves 25 clips. Results obtained are promising and we believe that this is a good baseline and will encourage future participation in such evaluations.

For the first time, search is enabled over a massive collection of 21 Million word images from digitized document images. This work advances the state-of-the-art on multiple fronts: i) document images are made searchable by textual queries, ii) content-level access is provided to document for search and retrieval, iii) a novel approach, that does not require an OCR, is adapted and validated iv) a suite of image processing and pattern classification algorithms are proposed to efficiently the process and v) the scalability of the solution is demonstrated over a of 500 digitised books consisting of 75,000 pages.

Character recognition based approaches yield poor results for developing search engines for Indian language document images, due to the complexity of the script and the poor quality of the documents. Recognition free approaches, based on word-spotting, are not directly scalable to large collections, due to the computational complexity of matching images in the feature space. For example, if it requires 1 mSec to match two images, the retrieval of documents to a single query, from a large collection like ours, would require close to a day’s time. In this paper we propose a novel automatic annotation based approach to provide textual description of document images. With a one time, offline computational effort, we are able to build a text-based retrieval system, over annotated images. This system has an interactive response time of about 0.01 second. However, we pay the price in the form of massive offline computation, which is performed on a cluster of 35 computers, for about a month. Our procedure is highly automatic, requiring minimal human intervention.

A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of . The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known.

In this work, we have proposed the use of a new feature to capture the weak-textured nature of remote sensing images. Combined with an automatic classifier, our texton histograms are robust to variations in scale, orientation and illumination conditions as illustrated experimentally. The classification accuracy is further improved using additional image driven features obtained by the application of a feature selection procedure.

This paper proposes a region based approach for image retrieval. We develop an algorithm to segment an image into fuzzy regions based on coefficients of multiscale wavelet packet transform. The wavelet based features are clustered using fuzzy C-means algorithm. The final cluster centroids which are the representative points, signify the color and texture properties of the preassigned number of classes. Fuzzy Topological relationships are computed from the final fuzzy partition matrix. The color and texture properties as indicated by centroids and spatial relations between the segmented regions are used together to provide overall characterization of an image. The closeness between two images are estimated from these properties. The performance of the system is demonstrated using different set of examples from general purpose image database to prove that, our algorithm can be used to generate meaningful descriptions about the contents of the images.

This paper proposes a content based image retrieval system that uses semantic labels for determining image similarity. Thus, it aims to bridge the semantic gap between human perception and low-level features. Our approach works in two stages. Image segments, obtained from a subset of images in the database by an adaptive -means clustering algorithm, are labelled manually during the training stage. The training information is used to label all the images in the database during the second stage. When a query is given, it is also segmented and each segment is labelled using the information available from the training stage. Similarity score between the query and a database image is based on the labels associated with the two images. Our results on two test databases show that region labelling helps in increasing the retrieval precision when compared to feature-based matching.

This paper addresses the problem of obtaining an accurate 3D reconstruction from multiple views. Taking inspiration from the recent successes of using strong prior knowledge for image segmentation, we propose a framework for 3D reconstruction which uses such priors to overcome the ambiguity inherent in this problem. Our framework is based on an object-specific Markov Random Field ()[10]. It uses a volumetric scene representation and integrates conventional reconstruction measures such as photo-consistency, surface smoothness and visual hull membership with a strong object-specific prior. Simple parametric models of objects will be used as strong priors in our framework. We will show how parameters of these models can be efficiently estimated by performing inference on the using dynamic graph cuts [7]. This procedure not only gives an accurate object reconstruction, but also provides us with information regarding the pose or state of the object being reconstructed. We will show the results of our method in reconstructing deformable and articulated objects.

This paper presents an efficient algorithm for disparity map computation with an adaptive window by establishing two frame stereo correspondence. Adaptive window based approach has a clear advantage of producing dense depth maps from stereo images. In recent years there has not been much research on adaptive window based approach due its high complexity and large computation time. Adaptive window based method selects an appropriate rectangular window by evaluating the local variation of the intensity and the disparity. Ideally the window need not be rectangular but to reduce algorithmic complexity and hence computation time, rectangular window is taken. There is a need for correction of errors introduced due to the rectangular window which is not dealt by the existing algorithm. To reduce this error, a method has been proposed which not only improves the disparity maps but also has a lesser computational complexity. To demonstrate the effectiveness of the algorithm the experimental results from synthetic and real image pairs (provided by middlebury research group) including ones with ground-truth values for quantitative comparison with the other methods are presented. The proposed algorithm outperforms most of the existing algorithms evaluated in the taxonomy of dense two frame stereo algorithms. The implementation has been done in C++. The algorithm has been tested with the standard stereo pairs which are used as benchmark for comparison of algorithms in the taxonomy implementation.