Anjana Roy

Bio: Anjana Roy is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Curvature & Hough transform. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

An Integrated Scheme for Compression and Interactive Access to Document Images

Abstract: We present an integrated scheme for document image compression which preserves the layout structure, and still allows the display of textual portions to adapt to the user preferences and screen area. We encode the layout structure of the document images in an XML representation. The textual components and picture components are compressed separately into different representations. We derive an SVG (scalable vector graphics) representation of the complete document image. Compression is achieved since the word-images are encoded using specifications for geometric primitives that compose a word. A document rendered from its SVG representation can be adapted for display and interactive access through common browsers on desktop as well as mobile devices. We demonstrate the effectiveness of the proposed scheme for document access

GFG-Based Compression and Retrieval of Document Images in Indian Scripts

Abstract: Indexing and retrieval of Indian language documents is an important problem. We present an interactive access scheme for Indian language document collection using techniques for word-image-based search. The compression and retrieval paradigm we propose is applicable even for those Indian scripts for which reliable OCR technology is not available. Our technique for word spotting is based on exploiting the geometrical features of the word image. The word image features are represented in the form of a graph called geometric feature graph (GFG). The GFG is encoded as a string which serves as a compressed representation of the word image skeleton. We have also augmented the GFG-based word image spotting with latent semantic analysis for more effective retrieval. The query is specified as a set of word images and the documents that best match with the query representation in the latent semantic space are retrieved. The retrieval paradigm is further enhanced to the conceptual level with the use of document image content-domain knowledge specified in the form of an ontology.

Analysis on niche genetic algorithm based nonparametric curve recognition

Abstract: Niche Genetic Algorithm (NGA) is proposed to recognize a disconnected nonparametric curve from a noisy binary image. The fitness function used in the NGA is derived from the hypothesis: Human Visual Tradition Model (HVTM). Sharing function based niche technique and elite-preserving strategy are utilized to preserve population variety for converging at the global optimum. It has the advantage of using a nonparametric method to extract disconnected curves from the noisy binary image other than the parametric method, which Hough Transform (HT) can conclude. The curve extracted by using the nonparametric method is verified by comparing the best strings respectively along rows and columns in the permutation-based encoding space. The curve length can be derived automatically from the image by calculating the accumulation of the distance between the neighbor tiles in the extracted curve. In this paper, it is analyzed that the odd order moments of the tiles in the raw image is more sensitive to the tiles with cracks other than tiles with only noise, the algorithm complexity is sensitive to encoding approach, the evolution converge characteristics are sensitive to sharing function and parameters in fitness function. Experimental results present that the approach was successfully used in pavement crack detection.