vSpeak: Edge detection based feature extraction for sign to text conversion

This paper presents 'vSpeak', the first initiative taken in Pakistan for ICT enabled conversion of dynamic Sign Urdu gestures into natural language sentences. To realize this, vSpeak has adopted a novel approach for feature extraction using edge detection and image compression which gives input to the Artificial Neural Network that recognizes the gesture. This technique caters for the blurred images as well. The training and testing is currently being performed on a dataset of 200 patterns of 20 words from Sign Urdu with target accuracy of 90% and above.

Localization of handwritten text in documents using moment invariants and Delaunay triangulation

This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of hand-written text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as hand-written using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text. We remove the noise components in the document as part of the preprocessing step so this method works well on noisy documents. The success rate of the method is found to be 86%. Also for specific hand-written elements such as signatures or similar text the accuracy is found to be even higher at 93%.

Image Heritage - The Temporal Dimension in Consumers' Corporate Image Constructions

Images and brands have been topics of great interest in both academia and practice for a long time. The company’s image, which in this study is considered equivalent to the actual corporate brand, has become a strategic issue and one of the company’s most valuable assets. In contrast to mainstream corporate branding research focusing on consumerimages as steered and managed by the company, in the present study a genuine consumer-focus is taken. The question is asked: how do consumers perceive the company, and especially, how are their experiences of the company over time reflected in the corporate image? The findings indicate that consumers’ corporate images can be seen as being constructed through dynamic relational processes based on a multifaceted network of earlier images from multiple sources over time. The essential finding is that corporate images have a heritage. In the thesis, the concept of image heritage is introduced, which stands for the consumer’s earlier company-related experiences from multiple sources over time. In other words, consumers construct their images of the company based on earlier recalled images, perhaps dating back many years in time. Therefore, corporate images have roots - an image heritage – on which the images are constructed in the present. For companies, image heritage is a key for understanding consumers, and thereby also a key for consumer-focused branding strategies and activities. As image heritage is the consumer’s interpretation base and context for image constructions here and now, branding strategies and activities that meet this consumer-reality has a potential to become more effective. This thesis is positioned in the tradition of The Nordic School of Marketing Thought and introduces a relational dynamic perspective into branding through consumers’ image heritage. Anne Rindell is associated to CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration.

Text localization and extraction from complex gray images

We propose two texture-based approaches, one involving Gabor filters and the other employing log-polar wavelets, for separating text from non-text elements in a document image. Both the proposed algorithms compute local energy at some information-rich points, which are marked by Harris' corner detector. The advantage of this approach is that the algorithm calculates the local energy at selected points and not throughout the image, thus saving a lot of computational time. The algorithm has been tested on a large set of scanned text pages and the results have been seen to be better than the results from the existing algorithms. Among the proposed schemes, the Gabor filter based scheme marginally outperforms the wavelet based scheme.

Binarization and localization of text images captured on a mobile phone camera

This paper proposes and compares four methods of binarzing text images captured using a camera mounted on a cell phone. The advantages and disadvantages(image clarity and computational complexity) of each method over the others are demonstrated through binarized results. The images are of VGA or lower resolution.

Quest for authenticity - representation of the Sami image in Christmas tourism

Tourism is one of important livelihoods in Lapland. Christmas tourism was launched in the early 1980s and it became a success story - being labelled as the most epochal tourism product in Finland. Hence, today Christmas tourists are one of the most significant foreign groups arriving to Lapland during the winter season and contributing considerably to the economics of the northeastern periphery of the EU. Christmas tourism concentrates around Father Christmas who uses reindeer for transportation. The Sämi are the only indigenous people in the EU. They are all stereotypically perceived to be reindeer herders. Somehow these three, that is, Santa Claus, reindeer and the Sämi, have been incorporated into same fairytale dominion. In practice, this has happened by using the most visible cultural but also significant identity marker of the Sämi, the Sämi costume. This, in turn, has created controversy over authenticity due to manners in which the costume is used in tourism - often in imitational, mismatched forms by non-Sämi. In this thesis, after relevant literature review I intend to establish how the Sâmi are represented in Christmas tourism through visual data consisting of ten images from three foreign sources. Then I clarify why and to whom it matters of how the Sâmi are represented in Christmas tourism with the aid of 65 questionnaires and nineteen expert interviews collected mainly in the Finnish Sâmi Home Region in October 2009. Through the multiplicity of the voices of various interest and ethnic groups and by using critical discourse analysis I attempt to give an overview of the respondents' opinions and look at some preliminary solutions to the controversy. Based on my data, the non-Sami appear to accept the Sami costume usage in Christmas tourism most readily. Consequently, respect and attitudinal changes have become the respondents' propositions in addition to common set of rules of how the Sami image could be appropriated without violating the integrity of the Sami people, or a similar system of S¿m¡ Duodji trademark guaranteeing the authenticity of the tourism products. Additionally, though half of the interviewees explicate Sami presence in Christmas tourism by adding local flavour to otherwise commercial enterprise, the other half see no rationale to connect facts with fiction, that is, the Sami with Santa Claus.

Quest for authenticity - representation of the Sami image in Christmas tourism

Tourism is one of important livelihoods in Lapland. Christmas tourism was launched in the early 1980s and it became a success story - being labelled as the most epochal tourism product in Finland. Hence, today Christmas tourists are one of the most significant foreign groups arriving to Lapland during the winter season and contributing considerably to the economics of the northeastern periphery of the EU. Christmas tourism concentrates around Father Christmas who uses reindeer for transportation. The Sämi are the only indigenous people in the EU. They are all stereotypically perceived to be reindeer herders. Somehow these three, that is, Santa Claus, reindeer and the Sämi, have been incorporated into same fairytale dominion. In practice, this has happened by using the most visible cultural but also significant identity marker of the Sämi, the Sämi costume. This, in turn, has created controversy over authenticity due to manners in which the costume is used in tourism - often in imitational, mismatched forms by non-Sämi. In this thesis, after relevant literature review I intend to establish how the Sâmi are represented in Christmas tourism through visual data consisting of ten images from three foreign sources. Then I clarify why and to whom it matters of how the Sâmi are represented in Christmas tourism with the aid of 65 questionnaires and nineteen expert interviews collected mainly in the Finnish Sâmi Home Region in October 2009. Through the multiplicity of the voices of various interest and ethnic groups and by using critical discourse analysis I attempt to give an overview of the respondents' opinions and look at some preliminary solutions to the controversy. Based on my data, the non-Sami appear to accept the Sami costume usage in Christmas tourism most readily. Consequently, respect and attitudinal changes have become the respondents' propositions in addition to common set of rules of how the Sami image could be appropriated without violating the integrity of the Sami people, or a similar system of S¿m¡ Duodji trademark guaranteeing the authenticity of the tourism products. Additionally, though half of the interviewees explicate Sami presence in Christmas tourism by adding local flavour to otherwise commercial enterprise, the other half see no rationale to connect facts with fiction, that is, the Sami with Santa Claus.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Performance analysis for geometrical attack on digital image watermarking

We present a technique for irreversible watermarking approach robust to affine transform attacks in camera, biomedical and satellite images stored in the form of monochrome bitmap images. The watermarking approach is based on image normalisation in which both watermark embedding and extraction are carried out with respect to an image normalised to meet a set of predefined moment criteria. The normalisation procedure is invariant to affine transform attacks. The result of watermarking scheme is suitable for public watermarking applications, where the original image is not available for watermark extraction. Here, direct-sequence code division multiple access approach is used to embed multibit text information in DCT and DWT transform domains. The proposed watermarking schemes are robust against various types of attacks such as Gaussian noise, shearing, scaling, rotation, flipping, affine transform, signal processing and JPEG compression. Performance analysis results are measured using image processing metrics.

Image Encryption with Space-filling Curves

Conventional encryption techniques are usually applicable for text data and often unsuited for encrypting multimedia objects for two reasons. Firstly, the huge sizes associated with multimedia objects make conventional encryption computationally costly. Secondly, multimedia objects come with massive redundancies which are useful in avoiding encryption of the objects in their entirety. Hence a class of encryption techniques devoted to encrypting multimedia objects like images have been developed. These techniques make use of the fact that the data comprising multimedia objects like images could in general be seggregated into two disjoint components, namely salient and non-salient. While the former component contributes to the perceptual quality of the object, the latter only adds minor details to it. In the context of images, the salient component is often much smaller in size than the non-salient component. Encryption effort is considerably reduced if only the salient component is encrypted while leaving the other component unencrypted. A key challenge is to find means to achieve a desirable seggregation so that the unencrypted component does not reveal any information about the object itself. In this study, an image encryption approach that uses fractal structures known as space-filling curves- in order to reduce the encryption overload is presented. In addition, the approach also enables a high quality lossy compression of images.

MAST: multi-script annotation toolkit for scenic text

This paper describes a semi-automatic tool for annotation of multi-script text from natural scene images. To our knowledge, this is the maiden tool that deals with multi-script text or arbitrary orientation. The procedure involves manual seed selection followed by a region growing process to segment each word present in the image. The threshold for region growing can be varied by the user so as to ensure pixel-accurate character segmentation. The text present in the image is tagged word-by-word. A virtual keyboard interface has also been designed for entering the ground truth in ten Indic scripts, besides English. The keyboard interface can easily be generated for any script, thereby expanding the scope of the toolkit. Optionally, each segmented word can further be labeled into its constituent characters/symbols. Polygonal masks are used to split or merge the segmented words into valid characters/symbols. The ground truth is represented by a pixel-level segmented image and a '.txt' file that contains information about the number of words in the image, word bounding boxes, script and ground truth Unicode. The toolkit, developed using MATLAB, can be used to generate ground truth and annotation for any generic document image. Thus, it is useful for researchers in the document image processing community for evaluating the performance of document analysis and recognition techniques. The multi-script annotation toolokit (MAST) is available for free download.