An investigation into the use of linguistic context in cursive script recognition by computer

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

NBS monograph

From Introduction: "This report is the first of a series intended to provide a selective overview of research and development efforts and requirements in the somewhat overlapping fields of the computer and information sciences and technologies. The projected series of reports will attempt to outline the probable range of R & D activities in the computer and information sciences and technologies through selective reviews of the literature and to develop a reasonable consensus with respect to the opinions of workers in these and potentially related fields as to areas of continuing R & D concern for research program planning or review in these areas.

Mobile Pen and Paper Interaction

Although smartphones, tablets and other mobile devices become increasingly popular, pen and paper continue to play an important role in mobile settings, such as note taking or creative discussions. However, information on paper documents remains static and usage practices involving sharing, researching, linking or in any other way digitally processing information on paper are hindered by the gap between the digital and physical worlds. A considerable body of research has leveraged digital pen technology in order to overcome this problem with respect to static settings, however, systematically neglecting the mobile domain. Only recently, several approaches began exploring the mobile domain and developing initial insights into mobile pen-and-paper interaction (mPPI), e.g., to publish digital sketches, [Cowan et al., 2011], link paper and digital artifacts, [Pietrzak et al., 2012] or compose music, [Tsandilas, 2012]. However, applications designed to integrate the most common mobile tools pen, paper and mobile devices, thereby combining the benefits of both worlds in a hybrid mPPI ensemble, are hindered by the lack of supporting infrastructures and limited theoretical understanding of interaction design in the domain. This thesis advances the field by contributing a novel infrastructural approach toward supporting mPPI. It allows applications employing digital pen technology in controlling interactive functionality while preserving mobile characteristics of pen and paper. In addition, it contributes a conceptual framework of user interaction in the domain suiting to serve as basis for novel mPPI toolkits. Such toolkits ease development of mPPI solutions by focusing on expressing interaction rather than designing user interfaces by means of rigid widget sets. As such, they provide the link between infrastructure and interaction in the domain. Lastly, this thesis presents a novel, empirically substantiated theory of interaction in hybrid mPPI ensembles. This theory informs interaction design of mPPI, ultimately allowing to develop compelling and engaging interactive systems employing this modality

Using assistive technology software to compensate for writing and reading impairments in aphasia

Background: Aphasia is a language impairment affecting approximately one third of people after stroke. It can disrupt speaking, comprehension, reading and writing. This thesis concerned people with aphasia (PWA) with spelling and writing impairments (some also had reading difficulties) but relatively preserved speech and comprehension. Aims: 1. Consider the evidence for writing treatment interventions with a systematic review of the literature; 2. Conduct a pilot study testing the acceptability of a compensatory narrative writing treatment intervention using assistive technology (AT) software; 3. Report an empirical study which designed, delivered and evaluated a program to train ten PWA to operate two mainstream AT packages (Dragon NaturallySpeaking™, a voice recognition software (VRS) to support writing via dictation, and ClaroRead™, supporting reading via auditory processing). The study tested whether: a) AT could be used to produce functional narrative writing, b) reading support promoted writing success c) the intervention could be customised to suit individual goals. Methods: 1. Systematic literature review Electronic databases were searched; 53 papers meeting inclusion criteria were identified. Data were extracted, papers were critically appraised and their findings described. 2. Pilot Ten week AT training with two PWA to test acceptability of the intervention, design training schedule and materials, and test quantitative assessments and qualitative data collection methods for the main study 3. Main study Design and setting: Small group study with mixed methods, repeated measures design. Assessments and AT training in participants’ homes or at City, University of London. Participants: Ten participants meeting eligibility criteria (over 18 years old, medically stable, no significant cognitive impairment, aphasia due to stroke, presenting with acquired dysgraphia) were recruited via convenience sampling. They were not receiving speech and language therapy, had no marked evidence of neuromuscular, structural or motor-speech impairments, nor self-reported history of developmental dyslexia. Measures and procedure. Participants received 7-10 one-hour individual training sessions. Screening (language, cognition) and diagnostic (single word writing, single word reading) assessments took place at T1 (first baseline). Outcome measures (narrative writing, reading comprehension, quality of life, mood) were taken at T1 and repeated at T2 (second baseline), T3 (end of intervention) and T4 (three month follow up). Participant observation occurred throughout training; qualitative semi-structured interviews, a social participation assessment and cognitive monitoring took place at T2, T3 and T4. Results: 1. Systematic literature review Writing treatments were effective but often focused on single word production and seldom tested functional generalisation. Most were single case or small case series studies with remediatory goals; few used qualitative methodologies or investigated the impact of reading deficits. All narrative writing therapies were delivered via technology. 2. Pilot The intervention was acceptable to participants. Training schedule and materials were created and refined; quantitative outcome measures were finalised; emphasis on participant observation was increased. 3. Main study: Keyboard narrative writing was significantly improved by AT (Friedman’s χ² (3) = 8.27, p = .041), as was keyboard reading comprehension (Friedman’s χ² (3) = 21.07, p < .001), indicating compensatory effects of both AT. There was no change over time in pen and paper assessments of writing or reading, indicating no remediatory effect. A wide range of written genres were produced. Social network size significantly increased. There were no significant changes in mood or quality of life. Individual success rates varied; diagnostic and observation data suggested contributing factors were attitude, creativity, preserved speech production skills, spectrum of other aphasic traits, therapeutic goals, and cognition. Conclusion: The compensatory customisable AT training was acceptable to eight of ten participants, and resulted in significantly improved narrative writing performance. Implications: Compensatory AT interventions serve as a useful adjunct to remediatory spelling interventions, and are particularly useful for supporting functional narrative writing

Power Optimization for Sensor Hubs in Biomedical Applications

The design and development of wearable inertial sensor systems for health monitoring has garnered a huge attention in the scientific community and the industry during the last years. Such platforms have a typical architecture and common building blocks to enable data collection, data processing and feedback restitution. In this thesis we analyze power optimization techniques that can be applied to such systems. When reducing power consumption in a wearable system, different trade-offs have to be inevitably faced. We thus propose software techniques that span from well known duty cycling, frequency scaling, data compression to new paradigm such as radio triggering, heterogeneous multi-core and context aware power management

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability