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Relevant bibliographies by topics / Handwriting data base

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Academic literature on the topic 'Handwriting data base'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Handwriting data base"

1

Ramdan, Jabril, Khairuddin Omar, Mohammad Faidzul, and Ali Mady. "Arabic Handwriting Data Base for Text Recognition." Procedia Technology 11 (2013): 580–84. http://dx.doi.org/10.1016/j.protcy.2013.12.231.

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Al Shalabi, Hasan M., Mowafak F. Hasan, and Abbas M. Ali. "A New Data Base Scheme Arabic Handwriting Recognition by Hopfield Neural Networks Algorithm." Journal of Computer Science 1, no. 2 (2005): 204–6. http://dx.doi.org/10.3844/jcssp.2005.204.206.

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YASUDA, H., K. TAKAHASHI, and T. MATSUMOTO. "A DISCRETE HMM FOR ONLINE HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 05 (2000): 675–88. http://dx.doi.org/10.1142/s021800140000043x.

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A fast HMM algorithm is proposed for online handwritten character recognition. After preprocessing the input strokes are discretized so that a discrete HMM is used. This particular discretization naturally leads to a simple procedure for assigning the initial state and state transition probabilities. In the training phase, complete marginelization with respect to state is not performed. A criterion based on normalized maximum likelihood ratio is given for deciding when to create a new model for the same character in the learning phase, in order to cope with stroke order variations and large shape variations. Experiments are done on the Kuchibue data base from Tokyo University of Agriculture and Technology. The algorithm appears to be very robust against stroke number variations and have reasonable robustness against stroke order variations and large shape variations. A drawback of the proposed algorithm is its memory requirement when the number of character classes and their associated models becomes large. Density tying is discussed in order to overcome this problem.

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Begum, Nasima, Md Azim Hossain Akash, Sayma Rahman, Jungpil Shin, Md Rashedul Islam, and Md Ezharul Islam. "User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model." Future Internet 13, no. 9 (2021): 231. http://dx.doi.org/10.3390/fi13090231.

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Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

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Nguyen, Hau Hung. "INPUTING STUDENTS’ SCORE BASED ON GIST FEATURES, SUPPORT VECTOR MACHINES AND TESSERACT." Scientific Journal of Tra Vinh University 1, no. 41 (2020): 77–85. http://dx.doi.org/10.35382/18594816.1.41.2020.646.

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Handwriting recogination plays an important role in data inputing and processing in the practice. This attracts much attention of many researchers in different fields. In this paper, a new algorithm is proposed by basing on GIST features, Support Vector Machines (SVM) and Tesseract for entering the score on students’ transcript form at Soc Trang Vocational College. The algorithm consists of two main works, i.e., recognizing students’code and recogziing handwritten digit. In the proposed algorithm, all regions of interest are determined and extract their dictint features with using tesseract and GIST. Then, these features are classified by SVM mechanism. Experimental results demonstrated that the proposed algorithm obtained high performance with accuracy up to 96,57% for students’ code and 93,55% for Handwritting scores. Average time was 7,9s per one transcript.

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Patil, Shashidhar, Dubeom Kim, Seongsill Park, and Youngho Chai. "Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3692876.

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We present a wireless-inertial-measurement-unit- (WIMU-) based hand motion analysis technique for handwriting recognition in three-dimensional (3D) space. The proposed handwriting recognition system is not bounded by any limitations or constraints; users have the freedom and flexibility to write characters in free space. It uses hand motion analysis to segment hand motion data from a WIMU device that incorporates magnetic, angular rate, and gravity sensors (MARG) and a sensor fusion algorithm to automatically distinguish segments that represent handwriting from nonhandwriting data in continuous hand motion data. Dynamic time warping (DTW) recognition algorithm is used to recognize handwriting in real-time. We demonstrate that a user can freely write in air using an intuitive WIMU as an input and hand motion analysis device to recognize the handwriting in 3D space. The experimental results for recognizing handwriting in free space show that the proposed method is effective and efficient for other natural interaction techniques, such as in computer games and real-time hand gesture recognition applications.

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Samsuryadi, Samsuryadi, Rudi Kurniawan, and Fatma Susilawati Mohamad. "Automated handwriting analysis based on pattern recognition: a survey." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 196. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp196-206.

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<span>Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. </span>

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PENG, BINBIN, WENYIN LIU, YIN LIU, GUANGLIN HUANG, ZHENGXING SUN, and XIANGYU JIN. "AN SVM-BASED INCREMENTAL LEARNING ALGORITHM FOR USER ADAPTATION OF SKETCH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 08 (2004): 1529–50. http://dx.doi.org/10.1142/s0218001404003769.

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User adaptation is a critical problem in the design of human-computer interaction systems. Many pattern recognition problems, such as handwriting/sketching recognition and speech recognition, are user dependent, since different users' handwritings, drawing styles, and accents are different. Therefore, the classifiers for these problems should provide the functionality of user adaptation so as to let each particular user experience better recognition accuracy according to his input habit/style. However, the user adaptation functionality requires the classifiers to have the incremental learning ability, by which the classifiers can adapt to the user quickly without too much computation cost. In this paper, an SVM-based incremental learning algorithm is presented to solve this problem for sketch recognition. Our algorithm utilizes only the support vectors instead of all the historical samples, and selects some important samples from all newly added samples as training data. The importance of a sample is measured according to its distance to the hyper-plane of the SVM classifier. Theoretical analysis, experimentation, and evaluation of our algorithm in our online graphics recognition system SmartSketchpad, are presented to show the effectiveness of this algorithm. According to our experiments, this algorithm can reduce both the training time and the required storage space for the training dataset to a large extent with very little loss of precision.

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Mucha, Jan, Jiri Mekyska, Zoltan Galaz, et al. "Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting." Applied Sciences 8, no. 12 (2018): 2566. http://dx.doi.org/10.3390/app8122566.

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Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

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Ahlawat, Savita, and Rahul Rishi. "A Genetic Algorithm Based Feature Selection for Handwritten Digit Recognition." Recent Patents on Computer Science 12, no. 4 (2019): 304–16. http://dx.doi.org/10.2174/2213275911666181120111342.

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Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost. Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature selection technique for handwritten digit recognition. Methods: A hybrid feature set of statistical and geometrical features is developed in order to get the effective feature set consist of local and global characteristics of sample digits. The method utilizes a genetic algorithm based feature selection for selecting best distinguishable features and k-nearest neighbour for evaluating the fitness of features of handwritten digit dataset. Results: The experiments are carried out on standard The Chars74K handwritten digit dataset and reported a 66% reduction in the original feature set without sacrificing the recognition accuracy. Conclusion: The experiment results show the effectiveness of the proposed approach.

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Dissertations / Theses on the topic "Handwriting data base"

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Haffner, Thomas. "SLUB präsentiert Handschriftendatenbank neu." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2006. http://nbn-resolving.de/urn:nbn:de:swb:14-1160062349050-51623.

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Pang, Bo. "Handwriting Chinese character recognition based on quantum particle swarm optimization support vector machine." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950620.

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熊星皓. "Handwriting beautification based on analysis of smartphone sensor data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/r3vcy9.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>103<br>In modern information age, smartphones are common commodities for most people. There are many sensors in the smartphone, these sensors store useful information between people’s behavior and smartphones. And in recent years, smartphone handwriting trajectory input methods are more common. Smartphone users are using their phones in different situations(For examples, walking, jogging, riding the bus, etc.). When people use smartphones to enter handwriting trajectory information, the unstable situations may cause difficulty in processing these handwriting trajectory information. We hope to use our existing visualization tool to analyze the relation between sensors and unrecognizable handwriting trajectory information. To help us observe the relation between handwriting trajectory information and smartphone sensor data immediately, we also implement the presentation of our existing visualization tool on smartphones. In addition, we collect the handwritten user data of users in the unsteady situations. Base on the observations of the environment and processed data, we can present the trend of the relation between the smartphone sensor and the touch position. We demonstrate our smooth method and regression model to beautify handwriting.

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Book chapters on the topic "Handwriting data base"

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Bilan, Stepan, Mykola Bilan, Andrii Bilan, and Sergii Bilan. "Analysis of the Dynamics of Handwriting for Biometric Personality Identification Based on Cellular Automata." In Biometric Identification Technologies Based on Modern Data Mining Methods. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48378-4_4.

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Ali Yüksel, Kamer. "Gestural Interaction with Mobile Devices Based on Magnetic Field." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4446-5.ch011.

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The theory of around device interaction (ADI) has recently gained a lot of attention in the field of human computer interaction (HCI). As an alternative to the classic data entry methods, such as keypads and touch screens, ADI founds a 3D user interface that extends to the peripheral area of a device. In this chapter, the authors introduce a revolutionary interaction framework that is based on the idea of ADI. The proposed method constitutes a touchless data entry system that is based on the interaction between the magnetic fields around a device and a properly shaped magnet. The magnetic field that surrounds the device is generated by a magnetic sensor (compass) that is embedded in the new generation of mobile phones such as Apple’s iPhone 3GS and 4G, and Google’s Nexus one. The user movements of the properly shaped magnet in front of the device, then, deforms the sensor’s original magnetic field pattern whereby we can constitute a new means of communication between the user and the device. Thus, the magnetic field encompassing the device plays the role of a communication channel and encodes the hand-movement patterns of the user into temporal changes of the sensor’s magnetic field. In the back-end of the communication, an engine samples the momentary status of the field during a trial and recognizes the user’s pattern by matching it against some pre-recorded templates. The proposed method has been tested in a variety of applications such as handwriting recognition, user authentication, gesture recognition, and some entertainment applications. The experimental results show that the proposed interface not only elevates the convenience of user-device interactions, but also shows very promising accuracies in a wide range of applications requiring user interactions.

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Conference papers on the topic "Handwriting data base"

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Zanardi de Freitas, Anderson, Lucas A. de Sousa Ribeiro, Osvaldo Negrini Neto, Jorge E. S. Sarkis, and Andressa N. Siqueira. "Optical-coherence-tomography-based algorithm for handwriting forensic analysis." In AI and Optical Data Sciences, edited by Ken-ichi Kitayama and Bahram Jalali. SPIE, 2020. http://dx.doi.org/10.1117/12.2543356.

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Gahmousse, Abdellatif, Abdeljalil Gattal, Chawki Djeddi, and Imran Siddiqi. "Handwriting based Personality Identification using Textural Features." In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). IEEE, 2020. http://dx.doi.org/10.1109/icdabi51230.2020.9325664.

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Hu, Huacheng, Dezhi Chen, and Jianbin Zheng. "Online Handwriting Signature Verification Based on Template Clustering." In EBDIT 2019: 2019 3rd International Workshop on Education, Big Data and Information Technology. ACM, 2019. http://dx.doi.org/10.1145/3352740.3352762.

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Li, Yujia, Kaisheng Yao, and Geoffrey Zweig. "Feedback-based handwriting recognition from inertial sensor data for wearable devices." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178375.

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Guo, Tianmei, Jiwen Dong, and Lei Wang. "Classification of Handwriting Number Based on PCANet Network with Data Augmentation." In 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/ameii-16.2016.135.

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Korovai, Karyna, and Oleksandr Marchenko. "Handwriting Styles Clustering: Feature Selection and Feature Space Analysis based on Online Input." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204338.

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Vajda, Szilard, and Gernot A. Fink. "Strategies for Training Robust Neural Network Based Digit Recognizers on Unbalanced Data Sets." In 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2010. http://dx.doi.org/10.1109/icfhr.2010.30.

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Frinken, Volkmar, Andreas Fischer, Horst Bunke, and R. Manmatha. "Adapting BLSTM Neural Network Based Keyword Spotting Trained on Modern Data to Historical Documents." In 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2010. http://dx.doi.org/10.1109/icfhr.2010.61.

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Costa-Abreu, Marjory da, and Michael Fairhurst. "Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.221.

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Liu, Zhe-Ting, Davy P. Y. Wong, and Pai H. Chou. "An Imu-Based Wearable Ring For On-Surface Handwriting Recognition." In 2020 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2020. http://dx.doi.org/10.1109/vlsi-dat49148.2020.9196479.

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