Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Painolliset äärellistilaiset menetelmät oikaisulukuun

This dissertation is a large-scale study of spell-checking and correction using finite-state technology. Finite-state spell-checking is a key method for handling morphologically complex languages in a computationally efficient manner. This dissertation discusses the technological and practical considerations that are required for finite-state spell-checkers to be at the same level as state-of-the-art non-finite-state spell-checkers. Three aspects of spell-checking are considered in the thesis: modelling of correctly written words and word-forms with finite-state language models, applying statistical information to finite-state language models with a specific focus on morphologically complex languages, and modelling misspellings and typing errors using finite-state automata-based error models. The usability of finite-state spell-checkers as a viable alternative to traditional non-finite-state solutions is demonstrated in a large-scale evaluation of spell-checking speed and the quality using languages with morphologically different natures. The selected languages display a full range of typological complexity, from isolating English to polysynthetic Greenlandic with agglutinative Finnish and the Saami languages somewhere in between.Tässä väitöskirjassa tutkin äärellistilaisten menetelmien käyttöä oikaisuluvussa. Äärellistilaiset menetelmät mahdollistavat sananmuodostukseltaan monimutkaisempien kielten, kuten suomen tai grönlannin, sanaston sujuvan käsittelyn oikaisulukusovelluksissa. Käsittelen tutkielmassani tieteellisiä ja käytännöllisiä toteutuksia, jotka ovat tarpeen, jotta tällaisia sananmuodostukseltaan monimutkallisempia kieliä voisi käsitellä oikaisuluvussa yhtä tehokkaasti kuin yksinkertaisempia kieliä, kuten englantia tai muita indo-eurooppalaisia kieliä nyt käsitellään. Tutkielmassa esitellään kolme keskeistä tutkimusongelmaa, jotka koskevat oikaisuluvun toteuttamista sanarakenteeltaan monimutkaisemmille kielille: miten mallintaa oikeinkirjoitetut sanamuodot äärellistilaisin mallein, miten soveltaa tilastollista mallinnusta monimutkaisiin sanarakenteisiin kuten yhdyssanoihin, ja miten mallintaa kirjoitusvirheitä äärellistilaisin mentelmin. Tutkielman tuloksena esitän äärellistilaisia oikaisulukumenetelmiä soveltuvana vaihtoehtona nykyisille oikaisulukimille, tämän todisteena esitän mittaustuloksia, jotka näyttävät, että käyttämäni menetelmät toimivat niin rakenteellisesti yksinkertaisille kielille kuten englannille yhtä hyvin kuin nykyiset menetelmät että rakenteellisesti monimutkaisemmille kielille kuten suomelle, saamelle ja jopa grönlannille riittävän hyvin tullakseen käytetyksi tyypillisissä oikaisulukimissa

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Spell checkers and correctors : a unified treatment

The aim of this dissertation is to provide a unified treatment of various spell checkers and correctors. Firstly, the spell checking and correcting problems are formally described in mathematics in order to provide a better understanding of these tasks. An approach that is similar to the way in which denotational semantics used to describe programming languages is adopted. Secondly, the various attributes of existing spell checking and correcting techniques are discussed. Extensive studies on selected spell checking/correcting algorithms and packages are then performed. Lastly, an empirical investigation of various spell checking/correcting packages is presented. It provides a comparison and suggests a classification of these packages in terms of their functionalities, implementation strategies, and performance. The investigation was conducted on packages for spell checking and correcting in English as well as in Northern Sotho and Chinese. The classification provides a unified presentation of the strengths and weaknesses of the techniques studied in the research. The findings provide a better understanding of these techniques in order to assist in improving some existing spell checking/correcting applications and future spell checking/correcting package designs and implementations.Dissertation (MSc)--University of Pretoria, 2009.Computer Scienceunrestricte

Understanding and designing for trust in Bitcoin Blockchain

Bitcoin is a cryptocurrency that has created a new revolution in peer-to-peer technology. Built upon decentralised technology known as Blockchain, it supports transparent, fast, cost-effective and irreversible transactions, without the need for trusting the third-party financial institution. The privacy of Bitcoin users is protected, by the pseudoanonymous transaction. At present, Bitcoin holds the largest market share in cryptocurrency and the Blockchain technology had captured the interest of multi-corporations, such as Microsoft, Dell, and T-Mobile. However, Bitcoins have no legal tender in most and it is even worse with the illicit use by the irresponsible people and the cyber-attacks towards the application. Hence, these are the primary motivation of this Ph.D. work, to explore the trust between people and Bitcoin technology as well as identify the opportunities to design for the trust challenges. This thesis investigates the challenges and design works with 80 Bitcoin stakeholders such as users, miners, Blockchain experts and novices in six different but interrelated studies. The first and second studies report in-depth preliminary studies with 20 Bitcoin users and 20 miners to identify the trust challenges in people’s daily practices in using Bitcoin. Based on the findings, users’ risk related to dishonest partner in peer-to-peer Bitcoins transactions is the highlighted trust challenges to be addressed in this thesis. With a strong understanding of Bitcoin mining process, a physical Blockchain design kit, namely BlocKit was developed based on the embodied cognition theories and material centred design. This BlocKit was evaluated by 15 Bitcoin Blockchain’s experienced users and one of the important outcomes proposed the principles to design for trust application in peer-to-peer Bitcoins transactions. Later the algorithms of trust for Bitcoin application were developed based on the suggested principles and were validated by 10 Bitcoin Blockchain’s experienced users. Finally, based on the designed algorithms as well as a newly identified heuristic evaluation for trust, a mock-up prototype of Bitcoin wallet application namely, BitXFps was developed and the interface was evaluated for trust by 15 Bitcoin Blockchain’s experienced users

Outrage: The Rise of Religious Offence in Contemporary South Asia

Whether spurred by religious images or academic history books, hardly a day goes by in South Asia without an incident or court case occurring as a result of hurt religious feelings. The sharp rise in blasphemy accusations over the past few decades calls for an investigation into why offence politics has become so pronounced, and why it is observable across religious and political differences. Outrage offers an interdisciplinary study of this growing trend. Bringing together researchers in Anthropology, Religious Studies, Languages, South Asia Studies and History, all with rich experience in the variegated ways in which religion and politics intersect in this region, the volume presents a fine-grained analysis that navigates and unpacks the religious sensitivities and political concerns under discussion. Each chapter focuses on a recent case or context of alleged blasphemy or desecration in India, Pakistan, Bangladesh and Myanmar, collectively exploring common denominators across national and religious differences. Among the common features are the rapid introduction of social media and smartphones, the possible political gains of initiating blasphemy accusations, and the growing self-assertion of marginal communities. These features are turning South Asia into a veritable flash point for offence controversies in the world today, and will be of interest to researchers exploring the intersection of religion and politics in South Asia and beyond