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.

Recognition of text from camera-captured scene/born-digital images help in the development of aids for the blind, unmanned navigation systems and spam filters. However, text in such images is not confined to any page layout, and its location within in the image is random in nature. In addition, motion blur, non-uniform illumination, skew, occlusion and scale-based degradations increase the complexity in locating and recognizing the text in a scene/born-digital image. Text localization and segmentation techniques are proposed for the born-digital image data set. The proposed OTCYMIST technique won the first place and placed in the third position for its performance on the text segmentation task in ICDAR 2011 and ICDAR 2013 robust reading competitions for born-digital image data set, respectively. Here, Otsu’s binarization and Canny edge detection are separately carried out on the three colour planes of the image. Connected components (CC’s) obtained from the segmented image are pruned based on thresholds applied on their area and aspect ratio. CC’s with sufficient edge pixels are retained. The centroids of the individual CC’s are used as nodes of a graph. A minimum spanning tree is built using these nodes of the graph. Long edges are broken from the minimum spanning tree of the graph. Pairwise height ratio is used to remove likely non-text components. CC’s are grouped based on their proximity in the horizontal direction to generate bounding boxes (BB’s) of text strings. Overlapping BB’s are removed using an overlap area threshold. Non-overlapping and minimally overlapping BB’s are used for text segmentation. These BB’s are split vertically to localize text at the word level. A word cropped from a document image can easily be recognized using a traditional optical character recognition (OCR) engine. However, recognizing a word, obtained by manually cropping a scene/born-digital image, is not trivial. Existing OCR engines do not handle these kinds of scene word images effectively. Our intention is to first segment the word image and then pass it to the existing OCR engines for recognition. In two aspects, it is advantageous: it avoids building a character classifier from scratch and reduces the word recognition task to a word segmentation task. Here, we propose two bottom-up approaches for the task of word segmentation. These approaches choose different features at the initial stage of segmentation. Power-law transform (PLT) was applied to the pixels of the gray scale born-digital images to non-linearly modify the histogram. The recognition rate achieved on born-digital word images is 82.9%, which is 20% more than the top performing entry (61.5%) in ICDAR 2011 robust reading competition. In addition, we explored applying PLT to the colour planes such as red, green, blue, intensity and lightness plane by varying the gamma value. We call this technique as Nonlinear enhancement and selection of plane (NESP) for optimal segmentation, which is an improvement over PLT. NESP chooses a particular plane with a proper gamma value based on Fisher discrimination factor. The recognition rate is 72.8% for scene images of ICDAR 2011 robust reading competition, which is 30% higher than the best entry (41.2%). The recognition rate is 81.7% and 65.9% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using NESP. Another technique, midline analysis and propagation of segmentation (MAPS), has also been proposed. Here, the middle row pixels of the gray scale image are first segmented and the statistics of the segmented pixels are used to assign text and non-text labels to the rest of the image pixels using min-cut method. Gaussian model is fitted on the middle row segmented pixels before the assignment of other pixels. In MAPS, we assume the middle row pixels are least affected by any of the degradations. This assumption is validated by the good word recognition rate of 71.7% on ICDAR 2011 robust reading competition for scene images. The recognition rate is 83.8% and 66.0% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using MAPS. The best reported results for ICDAR 2003 word images is 61.1% using custom lexicons containing the list of test words. On the other hand, NESP and MAPS achieve 66.2% and 64.5% for ICDAR 2003 word images without using any lexicon. By using similar custom lexicon, the recognition rates for ICDAR 2003 word images go up to 74.9% and 74.2% for NESP and MAPS methods, respectively. In place of passing an image segmented by a method, manually segmented word image is submitted to an OCR engine for benchmarking maximum possible recognition rate for each database. The recognition rates of the proposed methods and the benchmark results are reported on the seven publicly available word image data sets and compared with these of reported results in the literature. Since no good Kannada OCR is available, a classifier is designed to recognize Kannada characters and words from Chars74k data set and our own image collection, respectively. Discrete cosine transform (DCT) and block DCT are used as features to train separate classifiers. Kannada words are segmented using the same techniques (MAPS and NESP) and further segmented into groups of components, since a Kannada character may be represented by a single component or a group of components in an image. The recognition rate on Kannada words is reported for different features with and without the use of a lexicon. The obtained recognition performance for Kannada character recognition (11.4%) is three times the best performance (3.5%) reported in the literature.