Text is no longer confined to scanned pages and often appears in camera-based images originating from text on real world objects. Unlike the images from conventional flatbed scanners, which have a controlled acquisition environment, camera-based images pose new challenges such as uneven illumination, blur, poor resolution, perspective distortion and 3D deformations that can severely affect the performance of any optical character recognition (OCR) system. Due to the variations in the imaging condition as well as the target document type, traditional OCR systems, designed for scanned images, cannot be directly applied to camera-captured images and a new level of processing needs to be addressed. In this thesis, we study some of the issues commonly encountered in camera-based image analysis and propose novel methods to overcome them. All the methods make use of color connected components. 1. Connected component descriptor for document image mosaicing Document image analysis often requires mosaicing when it is not possible to capture a large document at a reasonable resolution in a single exposure. Such a document is captured in parts and mosaicing stitches them into a single image. Since connected components (CCs) in a document image can easily be extracted regardless of the image rotation, scale and perspective distortion, we design a robust feature named connected component descriptor that is tailored for mosaicing camera-captured document images. The method involves extraction of a circular measurement region around each CC and its description using the angular radial transform (ART). To ensure geometric consistency during feature matching, the ART coefficients of a CC are augmented with those of its 2 nearest neighbors. Our method addresses two critical issues often encountered in correspondence matching: (i) the stability of features and (ii) robustness against false matches due to multiple instances of many characters in a document image. We illustrate the effectiveness of the proposed method on camera-captured document images exhibiting large variations in viewpoint, illumination and scale. 2. Font and background color independent text binarization The first step in an OCR system, after document acquisition, is binarization, which converts a gray-scale/color image into a two-level image -the foreground text and the background. We propose two methods for binarization of color documents whereby the foreground text is output as black and the background as white regardless of the polarity of foreground-background shades. (a) Hierarchical CC Analysis: The method employs an edge-based connected component approach and automatically determines a threshold for each component. It overcomes several limitations of existing locally-adaptive thresholding techniques. Firstly, it can handle documents with multi-colored texts with different background shades. Secondly, the method is applicable to documents having text of widely varying sizes, usually not handled by local binarization methods. Thirdly, the method automatically computes the threshold for binarization and the logic for inverting the output from the image data and does not require any input parameter. However, the method is sensitive to complex backgrounds since it relies on the edge information to identify CCs. It also uses script-specific characteristics to filter out edge components before binarization and currently works well for Roman script only. (b) Contour-based color clustering (COCOCLUST): To overcome the above limitations, we introduce a novel unsupervised color clustering approach that operates on a ‘small’ representative set of color pixels identified using the contour information. Based on the assumption that every character is of a uniform color, we analyze each color layer individually and identify potential text regions for binarization. Experiments on several complex images having large variations in font, size, color, orientation and script illustrate the robustness of the method. 3. Multi-script and multi-oriented text extraction from scene images Scene text understanding normally involves a pre-processing step of text detection and extraction before subjecting the acquired image for character recognition task. The subsequent recognition task is performed only on the detected text regions so as to mitigate the effect of background complexity. We propose a color-based CC labeling for robust text segmentation from natural scene images. Text CCs are identified using a combination of support vector machine and neural network classifiers trained on a set of low-level features derived from the boundary, stroke and gradient information. We develop a semiautomatic annotation toolkit to generate pixel-accurate groundtruth of 100 scenic images containing text in various layout styles and multiple scripts. The overall precision, recall and f-measure obtained on our dataset are 0.8, 0.86 and 0.83, respectively. The proposed method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset, which, however, has only horizontal English text. The overall precision, recall and f-measure obtained are 0.63, 0.59 and 0.61 respectively, which is comparable to the best performing methods in the ICDAR 2005 text locating competition. A recent method proposed by Epshtein et al. [1] achieves better results but it cannot handle arbitrarily oriented text. Our method, however, works well for generic scene images having arbitrary text orientations. 4. Alignment of curved text lines Conventional OCR systems perform poorly on document images that contain multi-oriented text lines. We propose a technique that first identifies individual text lines by grouping adjacent CCs based on their proximity and regularity. For each identified text string, a B-spline curve is fitted to the centroids of the constituent characters and normal vectors are computed along the fitted curve. Each character is then individually rotated such that the corresponding normal vector is aligned with the vertical axis. The method has been tested on a data set consisting of 50 images with text laid out in various ways namely along arcs, waves, triangles and a combination of these with linearly skewed text lines. It yields 95.9% recognition accuracy on text strings, where, before alignment, state-of-the-art OCRs fail to recognize any text. The CC-based pre-processing algorithms developed are well-suited for processing camera-captured images. We demonstrate the feasibility of the algorithms on the publicly-available ICDAR 2003 robust reading competition dataset and our own database comprising camera-captured document images that contain multiple scripts and arbitrary text layouts.

With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.