Relevant bibliographies by topics / Online Handwritten Words

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  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"

1

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 authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.
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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 this limitation by enabling line-byline recognition and the conversion of handwritten text to OCR. To achieve this, the system utilizes the GRU algorithm to predict the next letter in incomplete words. Furthermore, the IAM dataset, consisting of 135,000 annotated sentences, is utilized to detect and rectify spelling errors in texts.
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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 numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.
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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"

1

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 circumstances, one must necessarily explore the possibility of segmenting a Tamil word to its individual symbols. Modern day Tamil alphabet comprises 23 consonants and 11 vowels forming a total combination of 313 characters/aksharas. A minimal set of 155 distinct symbols have been derived to recognize these characters. A corpus of isolated Tamil symbols (IWFHR database) is used for deriving the various statistics proposed in this work. To address the challenges of segmentation and recognition (the primary focus of the thesis), Tamil words are collected using a custom application running on a tablet PC. A set of 10000 words (comprising 53246 symbols) have been collected from high school students and used for the experiments in this thesis. We refer to this database as the ‘MILE word database’. In the first part of the work, a feedback based word segmentation mechanism has been proposed. Initially, the Tamil word is segmented based on a bounding box overlap criterion. This dominant overlap criterion segmentation (DOCS) generates a set of candidate stroke groups. Thereafter, attention is paid to certain attributes from the resulting stroke groups for detecting any possible splits or under-segmentations. By relying on feedbacks provided by a priori knowledge of attributes such as number of dominant points and inter-stroke displacements the recognition label and likelihood of the primary SVM classifier linguistic knowledge on the detected stroke groups, a decision is taken to correct it or not. Accordingly, we call the proposed segmentation as ‘attention feedback segmentation’ (AFS). Across the words in the MILE word database, a segmentation rate of 99.7% is achieved at symbol level with AFS. The high segmentation rate (with feedback) in turn improves the symbol recognition rate of the primary SVM classifier from 83.9% (with DOCS alone) to 88.4%. For addressing the problem of segmentation, the SVM classifier fed with the x-y trace of the normalized and resampled online stroke groups is quite effective. However, the performance of the classifier is not robust to effectively distinguish between many sets of similar looking symbols. In order to improve the symbol recognition performance, we explore two approaches, namely reevaluation strategies and language models. The reevaluation techniques, in particular, resolve the ambiguities in base consonants, pure consonants and vowel modifiers to a considerable extent. For the frequently confused sets (derived from the confusion matrix), a dynamic time warping (DTW) approach is proposed to automatically extract their discriminative regions. Dedicated to each confusion set, novel localized cues are derived from the discriminative region for their disambiguation. The proposed features are quite promising in improving the symbol recognition performance of the confusion sets. Comparative experimental analysis of these features with x-y coordinates are performed for judging their discriminative power. The resolving of confusions is accomplished with expert networks, comprising discriminative region extractor, feature extractor and SVM. The proposed techniques improve the symbol recognition rate by 3.5% (from 88.4% to 91.9%) on the MILE word database over the primary SVM classifier. In the final part of the thesis, we integrate linguistic knowledge (derived from a text corpus) in the primary recognition system. The biclass, bigram and unigram language models at symbol level are compared in terms of recognition performance. Amongst the three models, the bigram model is shown to give the highest recognition accuracy. A class reduction approach for recognition is adopted by incorporating the language bigram model at the akshara level. Lastly, a judicious combination of reevaluation techniques with language models is proposed in this work. Overall, an improvement of up to 4.7% (from 88.4% to 93.1%) in symbol level accuracy is achieved. The writer-independent and lexicon-free segmentation-recognition approach developed in this thesis for online handwritten Tamil word recognition is promising. The best performance of 93.1% (achieved at symbol level) is comparable to the highest reported accuracy in the literature for Tamil symbols. However, the latter one is on a database of isolated symbols (IWFHR competition test dataset), whereas our accuracy is on a database of 10000 words and thus, a product of segmentation and classifier accuracies. The recognition performance obtained may be enhanced further by experimenting on and choosing the best set of features and classifiers. Also, the word recognition performance can be very significantly improved by using a lexicon. However, these are not the issues addressed by the thesis. We hope that the lexicon-free experiments reported in this work will serve as a benchmark for future efforts.
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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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Abstract:
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 circumstances, one must necessarily explore the possibility of segmenting a Tamil word to its individual symbols. Modern day Tamil alphabet comprises 23 consonants and 11 vowels forming a total combination of 313 characters/aksharas. A minimal set of 155 distinct symbols have been derived to recognize these characters. A corpus of isolated Tamil symbols (IWFHR database) is used for deriving the various statistics proposed in this work. To address the challenges of segmentation and recognition (the primary focus of the thesis), Tamil words are collected using a custom application running on a tablet PC. A set of 10000 words (comprising 53246 symbols) have been collected from high school students and used for the experiments in this thesis. We refer to this database as the ‘MILE word database’. In the first part of the work, a feedback based word segmentation mechanism has been proposed. Initially, the Tamil word is segmented based on a bounding box overlap criterion. This dominant overlap criterion segmentation (DOCS) generates a set of candidate stroke groups. Thereafter, attention is paid to certain attributes from the resulting stroke groups for detecting any possible splits or under-segmentations. By relying on feedbacks provided by a priori knowledge of attributes such as number of dominant points and inter-stroke displacements the recognition label and likelihood of the primary SVM classifier linguistic knowledge on the detected stroke groups, a decision is taken to correct it or not. Accordingly, we call the proposed segmentation as ‘attention feedback segmentation’ (AFS). Across the words in the MILE word database, a segmentation rate of 99.7% is achieved at symbol level with AFS. The high segmentation rate (with feedback) in turn improves the symbol recognition rate of the primary SVM classifier from 83.9% (with DOCS alone) to 88.4%. For addressing the problem of segmentation, the SVM classifier fed with the x-y trace of the normalized and resampled online stroke groups is quite effective. However, the performance of the classifier is not robust to effectively distinguish between many sets of similar looking symbols. In order to improve the symbol recognition performance, we explore two approaches, namely reevaluation strategies and language models. The reevaluation techniques, in particular, resolve the ambiguities in base consonants, pure consonants and vowel modifiers to a considerable extent. For the frequently confused sets (derived from the confusion matrix), a dynamic time warping (DTW) approach is proposed to automatically extract their discriminative regions. Dedicated to each confusion set, novel localized cues are derived from the discriminative region for their disambiguation. The proposed features are quite promising in improving the symbol recognition performance of the confusion sets. Comparative experimental analysis of these features with x-y coordinates are performed for judging their discriminative power. The resolving of confusions is accomplished with expert networks, comprising discriminative region extractor, feature extractor and SVM. The proposed techniques improve the symbol recognition rate by 3.5% (from 88.4% to 91.9%) on the MILE word database over the primary SVM classifier. In the final part of the thesis, we integrate linguistic knowledge (derived from a text corpus) in the primary recognition system. The biclass, bigram and unigram language models at symbol level are compared in terms of recognition performance. Amongst the three models, the bigram model is shown to give the highest recognition accuracy. A class reduction approach for recognition is adopted by incorporating the language bigram model at the akshara level. Lastly, a judicious combination of reevaluation techniques with language models is proposed in this work. Overall, an improvement of up to 4.7% (from 88.4% to 93.1%) in symbol level accuracy is achieved. The writer-independent and lexicon-free segmentation-recognition approach developed in this thesis for online handwritten Tamil word recognition is promising. The best performance of 93.1% (achieved at symbol level) is comparable to the highest reported accuracy in the literature for Tamil symbols. However, the latter one is on a database of isolated symbols (IWFHR competition test dataset), whereas our accuracy is on a database of 10000 words and thus, a product of segmentation and classifier accuracies. The recognition performance obtained may be enhanced further by experimenting on and choosing the best set of features and classifiers. Also, the word recognition performance can be very significantly improved by using a lexicon. However, these are not the issues addressed by the thesis. We hope that the lexicon-free experiments reported in this work will serve as a benchmark for future efforts.
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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 kinds of information which is collected from 20 people wrote 10 times in different writing habits. The participants were asked to write on handwriting tablet, which will link with backend platform. Then the Handwritten Chinese Word Online Verification starts by probability Neural Network, which can verify the handwriting whether be written by himself or not. Besides, verifying the similarity by accuracy and error rate. We use Principal Components Analysis (PCA) and filter image noise by Gaussian Distribution as the preparation, than start the features analysis. We record the writing features of personal signatures; after that, in order to use Probability Neural Network to distinguish the writer, writing features links with backend platform.
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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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