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Relevant bibliographies by topics / Online Handwriting Recognition

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Academic literature on the topic 'Online Handwriting Recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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Journal articles on the topic "Online Handwriting Recognition"

1

Asrayev, Muhammadmullo. "ONLINE HANDWRITING RECOGNITION." Al-Farg'oniy avlodlari 1, no. 4 (2023): 142–46. https://doi.org/10.5281/zenodo.10336827.

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Handwriting is a natural way of putting information in legible form to be shared with readers. The scope and importance of handwriting is not all together out-shined with the creation of very sophisticated digital computers with facilitated input methods. In addition, for the new trend of small form factor computers and devices used for mobile computing, carrying a keyboard, even in miniaturized form, is becoming less and less of an option. It is particularly inconvenient to have keyboards in situations where one only has the need to jot down short notes. Another application is as a more natural and easier-to-use interface to the tasks involving complex formatting, like entering and editing equations, and drawing sketches and diagrams.

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VINITA, PATIL. "Review on Handwriting Recognition Techniques." Journal of Applied Science and Computations 5, no. 12 (2023): 1458–63. https://doi.org/10.5281/zenodo.7890740.

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- Handwriting is a skill that is unique to every individual. It was developed long time ago as a means to expand memory and facilitate communication. Handwriting of every person differs from every other. Every individual has their own style of writing. The understanding of handwriting generation is important in the development of both on-line and off-line recognition systems. Online handwriting recognition deals with information about writing dynamics as the text is being written while offline handwriting recognition deals with static information. This paper serves as a guide and updates the readers working on handwriting recognition. Here we are giving the review of some of the methods for handwriting recognition from selected papers

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3

Keysers, Daniel, Thomas Deselaers, Henry A. Rowley, Li-Lun Wang, and Victor Carbune. "Multi-Language Online Handwriting Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 6 (2017): 1180–94. http://dx.doi.org/10.1109/tpami.2016.2572693.

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Jianying Hu, M. K. Brown, and W. Turin. "HMM based online handwriting recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 10 (1996): 1039–45. http://dx.doi.org/10.1109/34.541414.

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Al-Helali, Baligh M., and Sabri A. Mahmoud. "Arabic Online Handwriting Recognition (AOHR)." ACM Computing Surveys 50, no. 3 (2017): 1–35. http://dx.doi.org/10.1145/3060620.

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Ghods, Vahid, and Ehsanollah Kabir. "A Study on Farsi Handwriting Styles for Online Recognition." Malaysian Journal of Computer Science 26, no. 1 (2013): 44–59. http://dx.doi.org/10.22452/mjcs.vol26no1.5.

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Knowing varieties of writing a letter in a word or a subword in different handwriting styles is very beneficial in recognition specifically for online recognition. In this paper, TMU-OFS dataset consisting of 1000 frequent Farsi subwords is employed to study Farsi handwriting styles. The subwords are grouped based on their delayed strokes and their main bodies, separately. The handwriting styles in this dataset are analyzed and the wrongly spelled or incorrect structural samples are extracted. Finally, the second version of the dataset is introduced by considering the handwriting styles. The preliminarily results show a significant improvement in recognition of subwords based on their styles.

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BIADSY, FADI, RAID SAABNI, and JIHAD EL-SANA. "SEGMENTATION-FREE ONLINE ARABIC HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 07 (2011): 1009–33. http://dx.doi.org/10.1142/s0218001411008956.

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Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

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Connell, S. D., and A. K. Jain. "Writer adaptation for online handwriting recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 3 (2002): 329–46. http://dx.doi.org/10.1109/34.990135.

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Bahlmann, Claus. "Directional features in online handwriting recognition." Pattern Recognition 39, no. 1 (2006): 115–25. http://dx.doi.org/10.1016/j.patcog.2005.05.012.

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Jaeger, S., S. Manke, J. Reichert, and A. Waibel. "Online handwriting recognition: the NPen++ recognizer." International Journal on Document Analysis and Recognition 3, no. 3 (2001): 169–80. http://dx.doi.org/10.1007/pl00013559.

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Dissertations / Theses on the topic "Online Handwriting Recognition"

1

Bahlmann, Claus [Verfasser]. "Advanced Sequence Classification Techniques Applied to Online Handwriting Recognition / Claus Bahlmann." Aachen : Shaker, 2005. http://d-nb.info/1186589868/34.

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Tsai, Tsung-Hsien, and 蔡宗憲. "Kinect-Based Online Handwriting Recognition System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/59581034508108841860.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>103<br>Vision-based mid-air handwriting recognition can be applied to many applications. For example, it can be used to control Apple’s iTV or serve as a text editor in motion-sensing game. This is also a challenging task. Unlike pen tablet can determine when system records trajectory or output result by touching panel, this paper have to design this two events for mid-air handwriting recognition system. Besides, the same words which are written by different users have different scale and style. This paper uses depth information via Kinect and uses OpenNI to extract human skeleton. In this paper, users do not need to use any gestures to start writing. When users finish writing, they just stay their hands in the end point of the word for one second to start recognition process. Owing to the design, the system must cause redundant trajectory. Because redundant trajectory is usually in the beginning of entire trajectory, we use architecture of backward combing segments to solve the problem of redundant trajectory. First of all, we use a method of detecting turning point to segment the trajectory. Then, we backward combine through segments from the last to first. Every combining segment is normalized to a uniform scale. Finally, we extract turning points, haar wavelet, dynamic time ordered shape context, and global time ordered shape context using dynamic time warping to match this features with database. Because some sub combining segments may be similar to some classes in the combing process, it may cause the system to find a wrong result. In general, the more the system combines segments, the more similar combined segment is to be. So this paper uses this property to collocate with a series of incremental weights. This paper uses a hierarchical classification to match with database more effectively so as to progressively find out optimal combined segment and class.

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Chiu, Chien-Chung, and 邱建忠. "New Results for Online Chinese Handwriting Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/92725176754688177928.

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碩士<br>國立雲林科技大學<br>電子與資訊工程研究所<br>93<br>We study in this thesis some methods to improve the performance of existing online Chinese handwriting recognition schemes. Concrete results include (1) the proposal of a hierarchical fuzzy clustering approach for online Chinese handwriting recognition, which is aimed at reduced recognition time; (2) the presentation of a radical-based neural network recognition system, which is particularly suitable for radicals consisting of connected strokes; and (3) an empirical study of a number of feature selection methods, which can be employed to increase recognition speed.

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Tsai, Ming-Yen, and 蔡旻諺. "Applications of the Point Distribution Model to Online Chinese Handwriting Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/06382142845461183075.

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碩士<br>國立雲林科技大學<br>電子與資訊工程研究所<br>93<br>In this thesis, we investigate the applications of the point distribution model (PDM) to online Chinese handwriting recognition. Three possibilities have been considered, including (1) an improved online Chinese handwritten character recognition scheme, (2) a generative model for online Chinese handwriting, and (3) a writer identification method using the PDM. High recognition rates were obtained from various experimental settings. These results confirm the applicability of the PDM approach.

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Stria, Jan. "Online rozpoznávání ručně psaných matematických formulí." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-305092.

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In this thesis, we deal with online recognition of handwritten mathematical formulas. We provide an introduction to the field of study, discuss the current state of the art and survey several existing implementations in detail. Then we present our own solution comprehending two steps. In the first step, individual symbols are detected utilizing temporal and spatial relations of the handwritten strokes. Then we recognize the symbols by combining results provided by an existing third party library with our rule based methods and template symbols matching. In the second step, a structure of the formula is examined. We introduce a novel approach incorporating a grammar based description of the formula with a statistical evaluation of the subexpressions. We also discuss related problems comprehending implementation of the recognizer as a web application and acquisition of the sample data.

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Nevolová, Kateřina. "Online rozpoznávání chemických vzorců a rovnic." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-347576.

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In this thesis, we deal with online recognition of handwritten chemical formulas and equations. First, we explain and point out some methods used, and present existing solutions. We examine the properties of the chemical structures. Then we describe our own solution consisting of detection of symbols on the basis of the spacial relations of strokes and their recognition provided by an existing third party library. After that the complex structure of the chemical formula or equation is examined using formal grammar, which we designed. The work also includes implementation of solutions in the form of web application and its experimental evaluation on our own collected set of test data. Powered by TCPDF (www.tcpdf.org)

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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 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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8

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 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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Book chapters on the topic "Online Handwriting Recognition"

1

Kim, JinHyung, and Bong-Kee Sin. "Online Handwriting Recognition." In Handbook of Document Image Processing and Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-0-85729-859-1_29.

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Bharath, A., and Sriganesh Madhvanath. "Online Handwriting Recognition for Indic Scripts." In Advances in Pattern Recognition. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-330-9_11.

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Qin, Xunhui, Hanyue Zhang, Xiao Ke, Zhonghao Shen, Songmao Qi, and Ke Liu. "Progressive Multitask Learning Network for Online Chinese Signature Segmentation and Recognition." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_11.

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Brakensiek, Anja, Andreas Kosmala, and Gerhard Rigoll. "Writer Adaptation for Online Handwriting Recognition." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_5.

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Ravindra Kumar, R., K. G. Sulochana, and T. R. Indhu. "Online Handwriting Recognition for Malayalam Script." In Information Systems for Indian Languages. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_32.

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Nilawar, Shradha, Madhav Vaidya, and Ganesh Pakle. "Online Handwriting Recognition in Multiple Languages." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0769-4_23.

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Huang, Bing Quan, Y. B. Zhang, and M. T. Kechadi. "Preprocessing Techniques for Online Handwriting Recognition." In Intelligent Text Categorization and Clustering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85644-3_2.

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Vorugunti, Chandra Sekhar, Balasubramanian Subramanian, Prerana Mukherjee, and Avinash Gautam. "COMPOSV++: Light Weight Online Signature Verification Framework Through Compound Feature Extraction and Few-Shot Learning." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_7.

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Yin, Zhixin, Shanxiong Chen, Dingwang Wang, Xihua Peng, and Jun Zhou. "Yi Characters Online Handwriting Recognition Models Based on Recurrent Neural Network: RnnNet-Yi and ParallelRnnNet-Yi." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_26.

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Vorugunti, Chandra Sekhar, Balasubramanian Subramanian, Avinash Gautam, and Viswanath Pulabaigari. "Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_6.

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Conference papers on the topic "Online Handwriting Recognition"

1

Al-Salman, AbdulMalik, and Haifa Alyahya. "Arabic online handwriting recognition." In IML 2017: International Conference on Internet of Things and Machine Learning. ACM, 2017. http://dx.doi.org/10.1145/3109761.3158377.

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Kherallah, Monji, Najiba Tagougui, Adel M. Alimi, Haikal El Abed, and Volker Margner. "Online Arabic Handwriting Recognition Competition." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.289.

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Karnchanapusak, Credit, Phattharasuda Suwannakat, Waroonorn Rakprasertsuk, and Natasha Dejdumrong. "Online Handwriting Thai Character Recognition." In 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization (CGIV). IEEE, 2009. http://dx.doi.org/10.1109/cgiv.2009.79.

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Samanta, Oendrila, Anandarup Roy, Ujjwal Bhattacharya, and Swapan K. Parui. "Script independent online handwriting recognition." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333964.

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Roy, K., N. Sharma, T. Pal, and U. Pal. "Online Bangla Handwriting Recognition System." In Proceedings of the Sixth International Conference. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772381_0018.

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Karteek Alahari, Satya Lahari Putrevu, and C. V. Jawahar. "Discriminant substrokes for online handwriting recognition." In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE, 2005. http://dx.doi.org/10.1109/icdar.2005.88.

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Huang, B. Q., Y. B. Zhang, and M. T. Kechadi. "Preprocessing Techniques for Online Handwriting Recognition." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.31.

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Huang, B. Q., Y. B. Zhang, and M. T. Kechadi. "Preprocessing Techniques for Online Handwriting Recognition." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.4389705.

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Golubitsky, Oleg, and Stephen M. Watt. "Online stroke modeling for handwriting recognition." In the 2008 conference of the center for advanced studies. ACM Press, 2008. http://dx.doi.org/10.1145/1463788.1463796.

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Aggarwal, Rajat, Sirnam Swetha, Anoop M. Namboodiri, Jayanthi Sivaswamy, and C. V. Jawahar. "Online handwriting recognition using depth sensors." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333924.

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