Relevant bibliographies by topics / Online Handwritten Words

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

Academic literature on the topic 'Online Handwritten Words'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Online Handwritten Words"

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Singh, Sukhdeep, and Anuj Sharma. "Online Handwritten Gurmukhi Words Recognition." ACM Transactions on Asian and Low-Resource Language Information Processing 18, no. 3 (2019): 1–55. http://dx.doi.org/10.1145/3282441.

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Naik, Vishal A., and Apurva A. Desai. "Online Handwritten Gujarati Word Recognition." International Journal of Computer Vision and Image Processing 9, no. 1 (2019): 35–50. http://dx.doi.org/10.4018/ijcvip.2019010103.

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In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The au
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., G. Mandal, and T. Biswas. "Slant Estimation and Correction for Online Handwritten Bengali Words." International Journal of Computer Sciences and Engineering 6, no. 5 (2018): 535–39. http://dx.doi.org/10.26438/ijcse/v6i5.535539.

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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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Kanmani, Dr S., B. Sujitha, K. Subalakshmi, S. Umamaheswari, and Karimreddy Punya Sai Teja Reddy. "Off-Line and Online Handwritten Character Recognition Using RNN-GRU Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2518–26. http://dx.doi.org/10.22214/ijraset.2023.50184.

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Abstract: Recognizing handwritten characters is an extremely difficult task in the domains of pattern recognition and computer vision. It involves the use of a process that enables computers to identify and convert handwritten or printed characters, such as letters and numbers, into a digital format that is usable by the computer. Currently, the RNN-CNN hybrid algorithm is employed to predict handwritten text in images with an accuracy rate of 91.5%. However, the existing system can only recognize characters and words character-by-character and word-by-word. The proposed system aims to address
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Ghosh, Rajib, and Gouranga Mandal. "A Novel Approach of Skew Correction for Online Handwritten Words." International Journal of Computer Applications 48, no. 9 (2012): 45–48. http://dx.doi.org/10.5120/7380-0304.

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Ghosh, Rajib, and Prabhat Kumar. "SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 200–214. http://dx.doi.org/10.2174/2213275912666181127124711.

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Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten num
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Thein, Yadana, and San Su Su Yee. "Online Myanmar Handwritten Compound Words Recognition and Erratum Detection with MICR." International Journal of Computer Applications 9, no. 6 (2010): 17–22. http://dx.doi.org/10.5120/1390-1873.

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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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Singh, Sukhdeep, Anuj Sharma, and Indu Chhabra. "Online Handwritten Gurmukhi Strokes Dataset Based on Minimal Set of Words." ACM Transactions on Asian and Low-Resource Language Information Processing 16, no. 1 (2016): 1–20. http://dx.doi.org/10.1145/2896318.

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Dissertations / Theses on the topic "Online Handwritten Words"

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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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Lin, Jia-He, and 林家禾. "Handwritten Chinese Word Online Verification by Neural Network." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/58408021312733670849.

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碩士<br>國立高雄海洋科技大學<br>電訊工程研究所<br>103<br>Biometrics is a hot topic recent years. In the field, Neural Network is the most useful Machine learning algorithms. As a result, it could be used in many fields. The target of this thesis is Handwritten Chinese Word Online Verification. This kind of verifications regards individual handwritten Chinese word as recognition unit, and records the handwriting; then analyzes the features of it. The Handwritten Chinese Word Online Verification combines recognition function algorithm and immediately backend platform as one platform. The data base concludes 200
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Book chapters on the topic "Online Handwritten Words"

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Krichen, Omar, Simon Corbillé, Eric Anquetil, Nathalie Girard, and Pauline Nerdeux. "Online Analysis of Children Handwritten Words in Dictation Context." In Document Analysis and Recognition – ICDAR 2021 Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86198-8_10.

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Singh, Harjeet, R. K. Sharma, Rajesh Kumar, Karun Verma, Ravinder Kumar, and Munish Kumar. "A Benchmark Dataset of Online Handwritten Gurmukhi Script Words and Numerals." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4018-9_41.

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Abuzaraida, Mustafa Ali, Akram M. Zeki, and Ahmed M. Zeki. "Online Recognition of Arabic Handwritten Words System Based on Alignments Matching Algorithm." In Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015). Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2772-7_5.

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Shiddiki, Nur-A.-Alam, and Mohammed Moshiul Hoque. "Developing a Fuzzy Feature-Based Online Bengali Handwritten Word Recognition System." In Proceedings of International Joint Conference on Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_46.

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Simayi, Wujiahemaiti, Mayire Ibrayim, and Askar Hamdulla. "A Study of RNN Based Online Handwritten Uyghur Word Recognition Using Different Word Transcriptions." In Simulation Tools and Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_50.

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Das, Abhishek, and Mihir Narayan Mohanty. "An Useful Review on Optical Character Recognition for Smart Era Generation." In Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4703-8.ch001.

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In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.
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Conference papers on the topic "Online Handwritten Words"

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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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Sesa-Nogueras, Enric. "Discriminative power of online handwritten words for writer recognition." In 2011 International Carnahan Conference on Security Technology (ICCST). IEEE, 2011. http://dx.doi.org/10.1109/ccst.2011.6095953.

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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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Sharma, Anuj, Rajesh Kumar, and R. K. Sharma. "Rearrangement of Recognized Strokes in Online Handwritten Gurmukhi Words Recognition." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.36.

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Faradji, Farhad, Karim Faez, and Mir Hashem Mousavi. "An HMM-based online recognition system for Farsi handwritten words." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658572.

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Srimany, A., S. Dutta Chowdhuri, U. Bhattacharya, and S. K. Parui. "Holistic Recognition of Online Handwritten Words Based on an Ensemble of SVM Classifiers." In 2014 11th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2014. http://dx.doi.org/10.1109/das.2014.67.

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Faradji, Farhad, Karim Faez, and Masoud S. Nosrati. "Online Farsi handwritten words recognition using a combination of 3 cascaded RBF neural networks." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658362.

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Ghosh, Rajib, Debnath Bhattacharyya, and Samir Kumar Bandyopadhyay. "Segmentation of Online Bangla Handwritten Word." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4809090.

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Sundaram, Suresh, Bhargava Urala K, and A. G. Ramakrishnan. "Language models for online handwritten Tamil word recognition." In Proceeding of the workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2432553.2432562.

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Kunwar, Rituraj, Shashikiran K., and A. G. Ramakrishnan. "Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary." In 2010 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010). IEEE, 2010. http://dx.doi.org/10.1109/icfhr.2010.100.

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