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The automatic recognition of hand-written text has been a goal for over thirty five years. The highly ambiguous nature of cursive writing (with high variability between not only different writers, but even between different samples from the same writer), means that systems based only on visual information are prone to errors. It is suggested that the application of linguistic knowledge to the recognition task may improve recognition accuracy. If a low-level (pattern recognition based) recogniser produces a candidate lattice (i.e. a directed graph giving a number of alternatives at each word position in a sentence), then linguistic knowledge can be used to find the 'best' path through the lattice. There are many forms of linguistic knowledge that may be used to this end. This thesis looks specifically at the use of collocation as a source of linguistic knowledge. Collocation describes the statistical tendency of certain words to co-occur in a language, within a defined range. It is suggested that this tendency may be exploited to aid automatic text recognition. The construction and use of a post-processing system incorporating collocational knowledge is described, as are a number of experiments designed to test the effectiveness of collocation as an aid to text recognition. The results of these experiments suggest that collocational statistics may be a useful form of knowledge for this application and that further research may produce a system of real practical use.

This research focused on the off-line cursive script recognition application. The problem is very large and difficult and there is much room for improvement in every aspect of the problem. Many different aspects of this problem were explored in pursuit of solutions to create a more practical and usable off-line cursive script recognizer than is currently available.

Texture analysis is an important problem in machine vision, with applications in many fields including medical imaging, remote sensing (SAR), automated flaw detection in various products, and document analysis to name but a few. Over the last four decades many techniques for the analysis of textured images have been proposed in the literature for the purposes of classification, segmentation, synthesis and compression. Such approaches include analysis the properties of individual texture elements, using statistical features obtained from the grey-level values of the image itself, random field models, and multichannel filtering. The wavelet transform, a unified framework for the multiresolution decomposition of signals, falls into this final category, and allows a texture to be examined in a number of resolutions whilst maintaining spatial resolution. This thesis explores the use of the wavelet transform to the specific task of texture classification and proposes a number of improvements to existing techniques, both in the area of feature extraction and classifier design. By applying a nonlinear transform to the wavelet coefficients, a better characterisation can be obtained for many natural textures, leading to increased classification performance when using first and second order statistics of these coefficients as features. In the area of classifier design, a combination of an optimal discriminate function and a non-parametric Gaussian mixture model classifier is shown to experimentally outperform other classifier configurations. By modelling the relationships between neighbouring bands of the wavelet trans- form, more information regarding a texture can be obtained. Using such a representation, an efficient algorithm for the searching and retrieval of textured images from a database is proposed, as well as a novel set of features for texture classification. These features are experimentally shown to outperform features proposed in the literature, as well as provide increased robustness to small changes in scale. Determining the script and language of a printed document is an important task in the field of document processing. In the final part of this thesis, the use of texture analysis techniques to accomplish these tasks is investigated. Using maximum a posterior (MAP) adaptation, prior information regarding the nature of script images can be used to increase the accuracy of these methods. Novel techniques for estimating the skew of such documents, normalising text block prior to extraction of texture features and accurately classifying multiple fonts are also presented.

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A document image, beside text, may contain pictures, graphs, signatures, logos, barcodes, hand-drawn sketches and/or seals. Further, the text blocks in an image may be in Manhattan or any complex layout. Document Layout Analysis is an important preprocessing step before subjecting any such image to OCR. Here, the image with complex layout and content is segmented into its constituent components. For many present day applications, separating the text from the non-text blocks is sufficient. This enables the conversion of the text elements present in the image to their corresponding editable form. In this work, an effort has been made to separate the text areas from the various kinds of possible non-text elements. The document images may have been obtained from a scanner or camera. If the source is a scanner, there is control on the scanning resolution, and lighting of the paper surface. Moreover, during the scanning process, the paper surface remains parallel to the sensor surface. However, when an image is obtained through a camera, these advantages are no longer available. Here, an algorithm is proposed to separate the text present in an image from the clutter, irrespective of the imaging technology used. This is achieved by using both the structural and textural information of the text present in the gray image. A bank of Gabor filters characterizes the statistical distribution of the text elements in the document. A connected component based technique removes certain types of non-text elements from the image. When a camera is used to acquire document images, generally, along with the structural and textural information of the text, color information is also obtained. It can be assumed that text present in an image has a certain amount of color homogeneity. So, a graph-theoretical color clustering scheme is employed to segment the iso-color components of the image. Each iso-color image is then analyzed separately for its structural and textural properties. The results of such analyses are merged with the information obtained from the gray component of the image. This helps to separate the colored text areas from the non-text elements. The proposed scheme is computationally intensive, because the separation of the text from non-text entities is performed at the pixel level Since any entity is represented by a connected set of pixels, it makes more sense to carry out the separation only at specific points, selected as representatives of their neighborhood. Harris' operator evaluates an edge-measure at each pixel and selects pixels, which are locally rich on this measure. These points are then employed for separating text from non-text elements. Many government documents and forms in India are bi-lingual or tri-lingual in nature. Further, in school text books, it is common to find English words interspersed within sentences in the main Indian language of the book. In such documents, successive words in a line of text may be of different scripts (languages). Hence, for OCR of these documents, the script must be recognized at the level of words, rather than lines or paragraphs. A database of about 20,000 words each from 11 Indian scripts1 is created. This is so far the largest database of Indian words collected and deployed for script recognition purpose. Here again, a bank of 36 Gabor filters is used to extract the feature vector which represents the script of the word. The effectiveness of Gabor features is compared with that of DCT and it is found that Gabor features marginally outperform the DOT. Simple, linear and non-linear classifiers are employed to classify the word in the feature space. It is assumed that a scheme developed to recognize the script of the words would work equally fine for sentences and paragraphs. This assumption has been verified with supporting results. A systematic study has been conducted to evaluate and compare the accuracy of various feature-classifier combinations for word script recognition. We have considered the cases of bi-script and tri-script documents, which are largely available. Average recognition accuracies for bi-script and tri-script cases are 98.4% and 98.2%, respectively. A hierarchical blind script recognizer, involving all eleven scripts has been developed and evaluated, which yields an average accuracy of 94.1%. The major contributions of the thesis are: • A graph theoretic color clustering scheme is used to segment colored text. • A scheme is proposed to separate text from the non-text content of documents with complex layout and content, captured by scanner or camera. • Computational complexity is reduced by performing the separation task on a selected set of locally edge-rich points. • Script identification at word level is carried out using different feature classifier combinations. Gabor features with SVM classifier outperforms any other feature-classifier combinations. A hierarchical blind script recognition algorithm, involving the recognition of 11 Indian scripts, is developed. This structure employs the most efficient feature-classifier combination at each individual nodal point of the tree to maximize the system performance. A sequential forward feature selection algorithm is employed to. select the most discriminating features, in a case by case basis, for script-recognition. The 11 scripts are Bengali, Devanagari, Gujarati, Kannada, Malayalam, Odiya, Puniabi, Roman. Tamil, Telugu and Urdu.