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.

Frechet distance based approach for searching online handwritten documents

We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.

Machine recognition of online handwritten Devanagari characters

In this paper, we describe a system for the automatic recognition of isolated handwritten Devanagari characters obtained by linearizing consonant conjuncts. Owing to the large number of characters and resulting demands on data acquisition, we use structural recognition techniques to reduce some characters to others. The residual characters are then classified using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system Ifs evaluated for the writer dependent scenario.

Dokumentation och beskrivning som språkplanering : perspektiv från arbete med tre tecknade minoritetsspråk

Language Documentation and Description as Language Planning Working with Three Signed Minority Languages Sign languages are minority languages that typically have a low status in society. Language planning has traditionally been controlled from outside the sign-language community. Even though signed languages lack a written form, dictionaries have played an important role in language description and as tools in foreign language learning. The background to the present study on sign language documentation and description as language planning is empirical research in three dictionary projects in Finland-Swedish Sign Language, Albanian Sign Language, and Kosovar Sign Language. The study consists of an introductory article and five detailed studies which address language planning from different perspectives. The theoretical basis of the study is sociocultural linguistics. The research methods used were participant observation, interviews, focus group discussions, and document analysis. The primary research questions are the following: (1) What is the role of dictionary and lexicographic work in language planning, in research on undocumented signed language, and in relation to the language community as such? (2) What factors are particular challenges in the documentation of a sign language and should therefore be given special attention during lexicographic work? (3) Is a conventional dictionary a valid tool for describing an undocumented sign language? The results indicate that lexicographic work has a central part to play in language documentation, both as part of basic research on undocumented sign languages and for status planning. Existing dictionary work has contributed new knowledge about the languages and the language communities. The lexicographic work adds to the linguistic advocacy work done by the community itself with the aim of vitalizing the language, empowering the community, receiving governmental recognition for the language, and improving the linguistic (human) rights of the language users. The history of signed languages as low status languages has consequences for language planning and lexicography. One challenge that the study discusses is the relationship between the sign-language community and the hearing sign linguist. In order to make it possible for the community itself to take the lead in a language planning process, raising linguistic awareness within the community is crucial. The results give rise to questions of whether lexicographic work is of more importance for status planning than for corpus planning. A conventional dictionary as a tool for describing an undocumented sign language is criticised. The study discusses differences between signed and spoken/written languages that are challenging for lexicographic presentations. Alternative electronic lexicographic approaches including both lexicon and grammar are also discussed. Keywords: sign language, Finland-Swedish Sign Language, Albanian Sign Language, Kosovar Sign Language, language documentation and description, language planning, lexicography

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.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Font and Size Independent OCR for Printed Kannada Documents using SVM Classifiers

The following topics were dealt with: document analysis and recognition; multimedia document processing; character recognition; document image processing; cheque processing; form processing; music processing; document segmentation; electronic documents; character classification; handwritten character recognition; information retrieval; postal automation; font recognition; Indian language OCR; handwriting recognition; performance evaluation; graphics recognition; oriental character recognition; and word recognition

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.

QUAD: quality assessment of documents

We propose a set of metrics that evaluate the uniformity, sharpness, continuity, noise, stroke width variance,pulse width ratio, transient pixels density, entropy and variance of components to quantify the quality of a document image. The measures are intended to be used in any optical character recognition (OCR) engine to a priori estimate the expected performance of the OCR. The suggested measures have been evaluated on many document images, which have different scripts. The quality of a document image is manually annotated by users to create a ground truth. The idea is to correlate the values of the measures with the user annotated data. If the measure calculated matches the annotated description,then the metric is accepted; else it is rejected. In the set of metrics proposed, some of them are accepted and the rest are rejected. We have defined metrics that are easily estimatable. The metrics proposed in this paper are based on the feedback of homely grown OCR engines for Indic (Tamil and Kannada) languages. The metrics are independent of the scripts, and depend only on the quality and age of the paper and the printing. Experiments and results for each proposed metric are discussed. Actual recognition of the printed text is not performed to evaluate the proposed metrics. Sometimes, a document image containing broken characters results in good document image as per the evaluated metrics, which is part of the unsolved challenges. The proposed measures work on gray scale document images and fail to provide reliable information on binarized document image.

OTCYMIST: Otsu-Canny minimal spanning tree for born-digital images

Text segmentation and localization algorithms are proposed for the born-digital image dataset. Binarization and edge detection are separately carried out on the three colour planes of the image. Connected components (CC's) obtained from the binarized image are thresholded based on their area and aspect ratio. CC's which contain sufficient edge pixels are retained. A novel approach is presented, where the text components are represented as nodes of a graph. Nodes correspond to the centroids of the individual CC's. Long edges are broken from the minimum spanning tree of the graph. Pair wise height ratio is also used to remove likely non-text components. A new minimum spanning tree is created from the remaining nodes. Horizontal grouping is performed on the CC's to generate bounding boxes of text strings. Overlapping bounding boxes are removed using an overlap area threshold. Non-overlapping and minimally overlapping bounding boxes are used for text segmentation. Vertical splitting is applied to generate bounding boxes at the word level. The proposed method is applied on all the images of the test dataset and values of precision, recall and H-mean are obtained using different approaches.

Recognition of Kannada characters extracted from scene images

In this paper, we describe a method for feature extraction and classification of characters manually isolated from scene or natural images. Characters in a scene image may be affected by low resolution, uneven illumination or occlusion. We propose a novel method to perform binarization on gray scale images by minimizing energy functional. Discrete Cosine Transform and Angular Radial Transform are used to extract the features from characters after normalization for scale and translation. We have evaluated our method on the complete test set of Chars74k dataset for English and Kannada scripts consisting of handwritten and synthesized characters, as well as characters extracted from camera captured images. We utilize only synthesized and handwritten characters from this dataset as training set. Nearest neighbor classification is used in our experiments.

Language models for online handwritten Tamil word recognition

N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.

Benchmarking recognition results on camera captured word image data sets

We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.

Transferência de renda e capital portador de juros: uma insidiosa captura

Esta dissertação tem como objeto de estudo a relação entre capital portador de juros e programas de transferência de renda na atualidade. O objetivo pretendido é a análise da dinâmica de organização desta forma de capital e seus desdobramentos na contemporaneidade, bem como os impactos e determinações impostas às políticas sociais em tempos de financeirização do capital. Partimos da hipótese de que os programas de transferência de renda contribuem para o processo de contra-reforma das políticas sociais, tanto pela focalização preconizada como pela priorização orçamentária, em detrimento dos demais programas e projetos da assistência social, além de permitirem a remuneração do capital portador de juros por meio dos recursos que destinam às instituições bancárias. A pesquisa realizada utilizou-se de análise documental e teórica com base na tradição marxista, buscando analisar criticamente as configurações das políticas sociais na atualidade e o papel das transferências de renda. Buscamos ainda analisar o orçamento público no âmbito federal referente à Seguridade Social e, em particular, aos programas de transferência de renda no âmbito da Assistência Social. Escolhemos estudar o orçamento da Renda Mensal Vitalícia, o Benefício de Prestação Continuada e o Programa Bolsa-Família, entre os anos de 2006 e 2009, analisando os recursos que estes destinam às instituições bancárias que operacionalizam o repasse dos benefícios. O resultado da pesquisa demonstrou que os programas de transferência de renda são estratégias de contra-reformar as políticas sociais ao incentivar o processo de assistencialização da Seguridade Social visto que apresentam um aumento contínuo e substancial no âmbito orçamentário e ao operarem com a seletividade do atendimento aos mais pobres na contramão dos princípios fundamentais presentes no texto constitucional. São ainda mecanismos de alimentação do capital portador de juros ao repassarem consideráveis somas de recursos para as instituições bancárias e inserirem a classe trabalhadora pobre no mundo das finanças.

Atravessando a Linha Vermelha: Programa Nova Baixada de Educação Infantil discutindo a diversidade étnico-racial e cultural na formação docente

Esta dissertação tem como objeto a incorporação do tema Diversidade Étnico-Racial e Cultural na formação docente para a Educação Infantil na Periferia. A partir da problematização do cotidiano enfocou-se questões como Racismo e Preconceito e a forma como são abordadas junto à Infância Pequena. Nesta pesquisa buscou-se analisar o desenvolvimento do Programa Nova Baixada de Educação Infantil e refletir sobre o lugar que ocupa nas políticas educacionais, tendo como campo de investigação a Baixada Fluminense. Orienta pelo propósito de compreender de que forma as discussões étnico-raciais e a diversidade cultural estão ou não inseridas nos espaços de formação adotou-se, metodologicamente, uma abordagem qualitativa, de natureza descritiva. As técnicas privilegiadas foram: análise documental, entrevistas estruturadas e semi-estruturadas. Os sujeitos da investigação foram docentes e gestores de instituições nas quais se implementaram o PNB, a saber: Creche Margarida da Silva Duarte e Vereador Nilo Dias Teixeira, ambas no bairro da Chatuba, em Mesquita, município emancipado da cidade de Nova Iguaçu em 1999. Fez-se levantar e analisar as contribuições da formação docente no processo de pensar o fazer educativo. O referencial teórico se fundamenta nos estudos de Trindade, Silva, Kramer, Faria, Lino e Hasenbalg que abordam o tema relações étnico-racial na educação infantil. Através de nossa pesquisa observou-se que há escassez de trabalhos que discutem essa questão, como também, nas matrizes curriculares dos cursos de formação de professores, onde a Educação Infantil ocupa um espaço de penumbra como objeto de reflexão. Por fim, conclui-se que o meio acadêmico se volta, predominantemente, para os aspectos desenvolvimentistas da formação infantil, relegando ao segundo plano, a discussão sobre a diversidade cultural, étnica e racial. No tocante às políticas públicas indicamos a pertinência da revisão, pelo Poder Público, dos critérios que orientam a definição de prioridades e que na prática se traduzem de modo muito limitado frente às conquistas mais recentes dos direitos de todas as crianças de 0 a 6 anos, entre eles, os de freqüentar creches e pré-escolas, lugar seu conquistado.