Relevant bibliographies by topics / Online Tamil Words - Segmentation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Online Tamil Words - Segmentation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Online Tamil Words - Segmentation"

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Sundaram, Suresh, and A. G. Ramakrishnan. "Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words." ACM Transactions on Asian Language Information Processing 12, no. 1 (2013): 1–25. http://dx.doi.org/10.1145/2425327.2425331.

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Geetha, K., and R. Vadivel. "Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure." Oriental journal of computer science and technology 10, no. 1 (2017): 114–19. http://dx.doi.org/10.13005/ojcst/10.01.15.

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Process of identifying the end points of the acoustic units of the speech signal is called speech segmentation. Speech recognition systems can be designed using sub-word unit like phoneme. A Phoneme is the smallest unit of the language. It is context dependent and tedious to find the boundary. Automated phoneme segmentation is carried in researches using Short term Energy, Convex hull, Formant, Spectral Transition Measure(STM), Group Delay Functions, Bayesian Information Criterion, etc. In this research work, STM is used to find the phoneme boundary of Tamil speech utterances. Tamil spoken wor
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Sen, Shibaprasad, Shubham Chowdhury, Mridul Mitra, Friedhelm Schwenker, Ram Sarkar, and Kaushik Roy. "A novel segmentation technique for online handwritten Bangla words." Pattern Recognition Letters 139 (November 2020): 26–33. http://dx.doi.org/10.1016/j.patrec.2018.02.008.

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Sen, Shibaprasad, Ankan Bhattacharyya, Ram Sarkar, and Kaushik Roy. "BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–16. http://dx.doi.org/10.1145/3464379.

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The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme sta
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Veldhuis, Dorina, and Jeanne Kurvers. "Offline segmentation and online language processing units." Units of Language – Units of Writing 15, no. 2 (2012): 165–84. http://dx.doi.org/10.1075/wll.15.2.03vel.

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Although metalinguistic (‘offline’) awareness of words as linguistic units has been related to literacy, it is still uncertain whether literacy also affects the units of language that people process unconsciously (‘online’). In this contribution, we first discuss the characteristics of offline and online tasks, opening up the perspective that such tasks vary in nature along a continuum ranging from more offline to more online. Subsequently, we present a study employing three relatively more offline and two more online tasks which we conducted among 83 preliterate and 121 literate children at D
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Sundaram, Suresh, and A. G. Ramakrishnan. "Bigram Language Models and Reevaluation Strategy for Improved Recognition of Online Handwritten Tamil Words." ACM Transactions on Asian and Low-Resource Language Information Processing 14, no. 2 (2015): 1–28. http://dx.doi.org/10.1145/2671014.

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Liu, Zhishuo, Qianhui Shen, Jingmiao Ma, and Ziqi Dong. "Research on comment target extracting in Chinese online shopping platform." International Journal of Crowd Science 2, no. 3 (2018): 247–58. http://dx.doi.org/10.1108/ijcs-09-2018-0019.

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Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comm
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Abuzaraida, Mustafa Ali, Mohammed Elmehrek, and Esam Elsomadi. "Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3584. http://dx.doi.org/10.11591/ijece.v11i4.pp3584-3592.

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With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by D
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Boal, Jaime, and Álvaro Sánchez-Miralles. "Online topological segmentation of visual sequences using the algebraic connectivity of graphs." Robotica 34, no. 10 (2015): 2400–2413. http://dx.doi.org/10.1017/s0263574715000053.

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SUMMARYIn the context of topological mapping, the automatic segmentation of an environment into meaningful and distinct locations is still regarded as an open problem. This paper presents an algorithm to extract places online from image sequences based on the algebraic connectivity of graphs or Fiedler value, which provides an insight into how well connected several consecutive observations are. The main contribution of the proposed method is that it is a theoretically supported alternative to tuning thresholds on similarities, which is a difficult task and environment dependent. It can accomm
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Zhang, Tiehu. "Recognition and Segmentation of English Long and Short Sentences Based on Machine Translation." International Journal of Emerging Technologies in Learning (iJET) 15, no. 01 (2020): 152. http://dx.doi.org/10.3991/ijet.v14i19.10182.

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With the advent of the information age, long sentences which include many words and have more complex structures.. The translation of long sentences in English-Chinese machine translation has always been the focus of research. In this study, 400 long sentences were randomly selected from NTCIR-9 patent corpus for testing the recognition and segmentation effects of regular match method and error-driven method, and the accuracy rate of the translation was compared on Baidu Online Translation Platform. The results demonstrated that the regular matching method was effective in recognizing and segm
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Dissertations / Theses on the topic "Online Tamil Words - Segmentation"

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Sundaram, Suresh. "Lexicon-Free Recognition Strategies For Online Handwritten Tamil Words." Thesis, 2011. https://etd.iisc.ac.in/handle/2005/2363.

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In this thesis, we address some of the challenges involved in developing a robust writer-independent, lexicon-free system to recognize online Tamil words. Tamil, being a Dravidian language, is morphologically rich and also agglutinative and thus does not have a finite lexicon. For example, a single verb root can easily lead to hundreds of words after morphological changes and agglutination. Further, adoption of a lexicon-free recognition approach can be applied to form-filling applications, wherein the lexicon can become cumbersome (if not impossible) to capture all possible names. Under such
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Sundaram, Suresh. "Lexicon-Free Recognition Strategies For Online Handwritten Tamil Words." Thesis, 2011. http://etd.iisc.ernet.in/handle/2005/2363.

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In this thesis, we address some of the challenges involved in developing a robust writer-independent, lexicon-free system to recognize online Tamil words. Tamil, being a Dravidian language, is morphologically rich and also agglutinative and thus does not have a finite lexicon. For example, a single verb root can easily lead to hundreds of words after morphological changes and agglutination. Further, adoption of a lexicon-free recognition approach can be applied to form-filling applications, wherein the lexicon can become cumbersome (if not impossible) to capture all possible names. Under such
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Book chapters on the topic "Online Tamil Words - Segmentation"

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Veltri, Giuseppe A. "Describing Human Behaviour Through Computational Social Science." In Handbook of Computational Social Science for Policy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16624-2_8.

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AbstractThe possibilities offered by digital and Computational Social Science can improve our understanding of human behaviour as never before. The availability of behavioural data in a society where the digital has been widely adopted is because of two reasons: first, the vast amount of digital traces produced by people in their daily lives and related behaviours and, second, the possibility of running online experiments that can cover a large segment of a target population (we have seen online experiments with hundreds of thousands of participants). This chapter will discuss the opportunity offered by online large behavioural experiments. The implications for policymakers of this shift are the possibility of having behavioural insights both across different societies and better understanding and capturing within a country heterogeneity. In other words, large-scale online experiments combined with computational methods allow for unprecedented cognitive and behavioural based segmentation.
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Conference papers on the topic "Online Tamil Words - Segmentation"

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Sundaram, Suresh, and A. G. Ramakrishan. "Lexicon-Free, Novel Segmentation of Online Handwritten Indic Words." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.237.

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Dahake, Devesh, R. K. Sharma, and Harjeet Singh. "On segmentation of words from online handwritten Gurmukhi sentences." In 2017 2nd International Conference on Man and Machine Interfacing (MAMI). IEEE, 2017. http://dx.doi.org/10.1109/mami.2017.8307870.

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Potrus, Moayad Yousif, Umi Kalthum Ngah, and Harsa Amylia Mat Sakim. "An effective segmentation method for single stroke online cursive Arabic words." In 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE). IEEE, 2010. http://dx.doi.org/10.1109/iccaie.2010.5735078.

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