Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary

In this paper, we propose a novel heuristic approach to segment recognizable symbols from online Kannada word data and perform recognition of the entire word. Two different estimates of first derivative are extracted from the preprocessed stroke groups and used as features for classification. Estimate 2 proved better resulting in 88% accuracy, which is 3% more than that achieved with estimate 1. Classification is performed by statistical dynamic space warping (SDSW) classifier which uses X, Y co-ordinates and their first derivatives as features. Classifier is trained with data from 40 writers. 295 classes are handled covering Kannada aksharas, with Kannada numerals, Indo-Arabic numerals, punctuations and other special symbols like $ and #. Classification accuracies obtained are 88% at the akshara level and 80% at the word level, which shows the scope for further improvement in segmentation algorithm

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Seminar: Symbol Management systems to support democracy in development of an online Symbol Dictionary

Abstract This seminar will introduce an initial year of research exploring participation in the development of a bilingual symbol dictionary. Symbols can be a communication and literacy ‘lifeline’ for those unable to communicate through speech or writing. We will discuss how an online system has been built to overcome language, cultural and literacy skill issues for a country where 86% are expatriates but the target clients are Arabic born individuals with speech and language impairments. The symbols in use at present are inappropriate and yet there is no democratic way of providing a ‘user voice’ for making choices, let alone easy mechanisms for adapting and sharing newly developed symbols across the nation or extended Arabic world. This project aims to change this situation. Having sourced a series of symbols that could be adapted to suit user’s needs, the team needed to encourage those users, their carers and therapists to vote on whether the symbols would be appropriate and work with those already in use. The first prototype was developed and piloted during the WAISfest in 2013. The second phase needs further voting on the most suitably adapted symbols for use when communicating with others. There is a requirement to have mechanisms for evaluating the outcome of the votes, where symbols fail to represent accurate meanings, have inappropriate colours, representations and actions etc. There also remains the need to collect both quantitative and qualitative data. Not easy in a climate of acceptance of the expert view, a culture where to be critical can be a problem and time is not of the essence.

Between the headlines of the Israeli - Arabic conflict: the coverage of CNN and Al Jazeera

The purpose of this article is to analyze the coverage made by CNN and Al Jazeera (in Arabic) to operation Caste Lead and the Goldstone Report during 2008 and 2009. This investigation is based in the theory of Qualitative Analysis of Content, by Wildemuth and Zhang. The methodology follows up with the one proposed by the authors in the main theory, complementing it with the Gamson and Modigliani´s Framing theory. The methodology mention above display the different in the coverage development, determined by the geopolitical influences; being CNN more influenced by a Western pro USA and pro Israeli speech, while Al Jazeera is more prone to support the Palestinian cause, this is the thesis of this article. During the development of the investigation, the thesis was demonstrated to be only partially accurate as CNN was not completely supportive to the Israeli arguments during the coverage, but Al Jazeera did have preferential speech for the Palestinian cause.

Evolutions of an Online Generation: Education, Industry and IMC

This paper traces the evolutions of a new generation of students who are predominantly the ‘online generation’; explores the emerging impact of this generation on industry; identifies the changing role of education from traditional classroom to an online environment; and explores the contribution related to integrated marketing communications (IMC). Educational requirements from a business perspective must incorporate global business demands; virtual learning environments progress the online generation towards a post-modern learning state. The central proposition of this paper is that the emergence of IMC in evolving industry practices is influenced by student generations who are producing a new paradigm of alignment between education and industry. This is purely a conceptual exploration using limited examples to provide some context and illustrate the questions raised for consideration.