Texture for Script identification

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.

The effects of L1 orthography on processing an artificial logographic script

To date, studies have focused on the acquisition of alphabetic second languages (L2s) in alphabetic first language (L1) users, demonstrating significant transfer effects. The present study examined the process from a reverse perspective, comparing logographic (Mandarin-Chinese) and alphabetic (English) L1 users in the acquisition of an artificial logographic script, in order to determine whether similar language-specific advantageous transfer effects occurred. English monolinguals, English-French bilinguals and Chinese-English bilinguals learned a small set of symbols in an artificial logographic script and were subsequently tested on their ability to process this script in regard to three main perspectives: L2 reading, L2 working memory (WM), and inner processing strategies. In terms of L2 reading, a lexical decision task on the artificial symbols revealed markedly faster response times in the Chinese-English bilinguals, indicating a logographic transfer effect suggestive of a visual processing advantage. A syntactic decision task evaluated the degree to which the new language was mastered beyond the single word level. No L1-specific transfer effects were found for artificial language strings. In order to investigate visual processing of the artificial logographs further, a series of WM experiments were conducted. Artificial logographs were recalled under concurrent auditory and visuo-spatial suppression conditions to disrupt phonological and visual processing, respectively. No L1-specific transfer effects were found, indicating no visual processing advantage of the Chinese-English bilinguals. However, a bilingual processing advantage was found indicative of a superior ability to control executive functions. In terms of L1 WM, the Chinese-English bilinguals outperformed the alphabetic L1 users when processing L1 words, indicating a language experience-specific advantage. Questionnaire data on the cognitive strategies that were deployed during the acquisition and processing of the artificial logographic script revealed that the Chinese-English bilinguals rated their inner speech as lower than the alphabetic L1 users, suggesting that they were transferring their phonological processing skill set to the acquisition and use of an artificial script. Overall, evidence was found to indicate that language learners transfer specific L1 orthographic processing skills to L2 logographic processing. Additionally, evidence was also found indicating that a bilingual history enhances cognitive performance in L2.

Script proposals: a device for empowering clients in counselling

Much of the research on the delivery of advice by professionals such as physicians, health workers and counsellors, both on the telephone and in face to face interaction more generally, has focused on the theme of client resistance and the consequent need for professionals to adopt particular formats to assist in the uptake of the advice. In this paper we consider one setting, Kid’s Helpline, the national Australian counselling service for children and young people, where there is an institutional mandate not to give explicit advice in accordance with the values of self-direction and empowerment. The paper examines one practice, the use of script proposals by counsellors, which appears to offer a way of providing support which is consistent with these values. Script proposals entail the counsellors packaging their advice as something that the caller might say – at some future time – to a third party such as a friend, teacher, parent, or partner, and involve the counsellor adopting the speaking position of the caller in what appears as a rehearsal of a forthcoming strip of interaction. Although the core feature of a script proposal is the counsellor’s use of direct reported speech they appear to be delivered, not so much as exact words to be followed, but as the type of conversation that the client needs to have with the 3rd party. Script proposals, in short, provide models of what to say as well as alluding to how these could be emulated by the client. In their design script proposals invariably incorporate one or more of the most common rhetorical formats for maximising the persuasive force of an utterance such as a three part list or a contrastive pair. Script proposals, moreover, stand in a complex relation to the prior talk and one of their functions appears to be to summarise, respecify or expand upon the client’s own ideas or suggestions for problem solving that have emerged in these preceding sequences.

The acquisition of an artificial logographic script and bilingual working memory : Evidence for L1-specific orthographic processing skills transfer in Chinese-English bilinguals

Studies of orthographic skills transfer between languages focus mostly on working memory (WM) ability in alphabetic first language (L1) speakers when learning another, often alphabetically congruent, language. We report two studies that, instead, explored the transferability of L1 orthographic processing skills in WM in logographic-L1 and alphabetic-L1 speakers. English-French bilingual and English monolingual (alphabetic-L1) speakers, and Chinese-English (logographic-L1) speakers, learned a set of artificial logographs and associated meanings (Study 1). The logographs were used in WM tasks with and without concurrent articulatory or visuo-spatial suppression. The logographic-L1 bilinguals were markedly less affected by articulatory suppression than alphabetic-L1 monolinguals (who did not differ from their bilingual peers). Bilinguals overall were less affected by spatial interference, reflecting superior phonological processing skills or, conceivably, greater executive control. A comparison of span sizes for meaningful and meaningless logographs (Study 2) replicated these findings. However, the logographic-L1 bilinguals’ spans in L1 were measurably greater than those of their alphabetic-L1 (bilingual and monolingual) peers; a finding unaccounted for by faster articulation rates or differences in general intelligence. The overall pattern of results suggests an advantage (possibly perceptual) for logographic-L1 speakers, over and above the bilingual advantage also seen elsewhere in third language (L3) acquisition.

Automatic phonetic transcription of Tamil in Roman script

Simple formalized rules are proposed for automatic phonetic transcription of Tamil words into Roman script. These rules are syntax-directed and require a one-symbol look-ahead facility and hence easily automated in a digital computer. Some suggestions are also put forth for the linearization of Tamil script for handling these by modern machinery.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

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.

Multi-script and multi-oriented text localization from scene images

This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.

Recognition of open vocabulary, online handwritten pages in Tamil script

In this work, we describe a system, which recognises open vocabulary, isolated, online handwritten Tamil words and extend it to recognize a paragraph of writing. We explain in detail each step involved in the process: segmentation, preprocessing, feature extraction, classification and bigram-based post-processing. On our database of 45,000 handwritten words obtained through tablet PC, we have obtained symbol level accuracy of 78.5% and 85.3% without and with the usage of post-processing using symbol level language models, respectively. Word level accuracies for the same are 40.1% and 59.6%. A line and word level segmentation strategy is proposed, which gives promising results of 100% line segmentation and 98.1% word segmentation accuracies on our initial trials of 40 handwritten paragraphs. The two modules have been combined to obtain a full-fledged page recognition system for online handwritten Tamil data. To the knowledge of the authors, this is the first ever attempt on recognition of open vocabulary, online handwritten paragraphs in any Indian language.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

[Book reviews] Review of J. E. Hoch, Semitic Words in Egyptian Texts of the New Kingdom and Third Intermediate Period and D. Sivan and Z. Cochavi-Rainey, West Semitic Vocabulary in Egyptian Script of the 14th to the 10th Centuries BCE

Reviews of: [1] James E. Hoch, Semitic Words in Egyptian Texts of the New Kingdom and Third Intermediate Period, (1994), Princeton University Press. [2] Daniel Sivan and Zipora Cochavi-Rainey, West Semitic Vocabulary in Egyptian Script of the 14th to the 10th Centuries BCE, (1992), Ben-Gurion University of the Negev Press.

Effectiveness of medicines review with web-based pharmaceutical treatment algorithms in reducing potentially inappropriate prescribing in older people in primary care:a cluster randomized trial (OPTI-SCRIPT study protocol)

Potentially inappropriate prescribing in older people is common in primary care and can result in increased morbidity, adverse drug events, hospitalizations and mortality. In Ireland, 36% of those aged 70 years or over received at least one potentially inappropriate medication, with an associated expenditure of over €45 million.The main objective of this study is to determine the effectiveness and acceptability of a complex, multifaceted intervention in reducing the level of potentially inappropriate prescribing in primary care.

Addressing potentially inappropriate prescribing in older patients: development and pilot study of an intervention in primary care (the OPTI-SCRIPT study)

Background: Potentially inappropriate prescribing (PIP) in older people is common in primary care and can result in increased morbidity, adverse drug events, hospitalizations and mortality. The prevalence of PIP in Ireland is estimated at 36% with an associated expenditure of over [euro sign]45 million in 2007. The aim of this paper is to describe the application of the Medical Research Council (MRC) framework to the development of an intervention to decrease PIP in Irish primary care.

Methods: The MRC framework for the design and evaluation of complex interventions guided the development of the study intervention. In the development stage, literature was reviewed and combined with information obtained from experts in the field using a consensus based methodology and patient cases to define the main components of the intervention. In the pilot stage, five GPs tested the proposed intervention. Qualitative interviews were conducted with the GPs to inform the development and implementation of the intervention for the main randomised controlled trial.

Results: The literature review identified PIP criteria for inclusion in the study and two initial intervention components - academic detailing and medicines review supported by therapeutic treatment algorithms. Through patient case studies and a focus group with a group of 8 GPs, these components were refined and a third component of the intervention identified - patient information leaflets. The intervention was tested in a pilot study. In total, eight medicine reviews were conducted across five GP practices. These reviews addressed ten instances of PIP, nine of which were addressed in the form of either a dose reduction or a discontinuation of a targeted medication. Qualitative interviews highlighted that GPs were receptive to the intervention but patient preference and time needed both to prepare for and conduct the medicines review, emerged as potential barriers. Findings from the pilot study allowed further refinement to produce the finalised intervention of academic detailing with a pharmacist, medicines review with web-based therapeutic treatment algorithms and tailored patient information leaflets.

Conclusions: The MRC framework was used in the development of the OPTI-SCRIPT intervention to decrease the level of PIP in primary care in Ireland. Its application ensured that the intervention was developed using the best available evidence, was acceptable to GPs and feasible to deliver in the clinical setting. The effectiveness of this intervention is currently being tested in a pragmatic cluster randomised controlled trial.

Trial registration: Current controlled trials ISRCTN41694007.© 2013 Clyne et al.; licensee BioMed Central Ltd.