In-air handwritten Chinese character recognition with locality-sensitive sparse representation toward optimized prototype classifier

Qu, Xiwen; Wang, Weiqiang; Lü, Ke; Zhou, Jianshe

doi:10.1016/j.patcog.2018.01.021

Cited by 28 publications

(9 citation statements)

References 23 publications

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“…We have also paid attention to IAHCC-UCAS2016 dataset which is an in-air handwritten Chinese character dataset and is applied in previous many research work for character recognition. Each character in the IAHCC-UCAS2016 was written in a single stroke not including any pen-up/pen-down information [25], [35], [36]. However, the construction of conceptual model depends on the relationship among strokes which has s requirement of a stroke starting/ending point information.…”

Section: Comparison With Different Methodsmentioning

confidence: 99%

Recognition of Handwritten Chinese Characters Based on Concept Learning

Liang

Wang

et al. 2019

IEEE Access

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Many deep-learning character recognition methods have been developed over the past few years. Chinese characters are widely used in many countries; however, the deep-learning-based Chinese character recognition methods are faced with various problems, such as a large amount of data required for training, numerous parameters, and a large consumption of computing resources. Concept learning is a hominine learning approach. Unlike existing deep-learning models, conceptual model learning can be realized by using as little as one sample. This paper is the first to propose a handwritten Chinese character recognition method based on concept learning. Different from the existing image representation-based character recognition methods, the proposed method builds a meta stroke library with prior knowledge, and then, presents a Chinese character conceptual model based on stroke relationship learning using a character stroke extraction method and Bayesian program learning. During character recognition, Monte Carlo Markov chain sampling is utilized to obtain the character generation model for each character conceptual. This generation model can calculate the probability of the target and training characters being the same classification, and thereby determines the classification of the target character. The experimental results indicate that, with the proposed method, the conceptual model of each character can be built for character classification prediction using as few as one character sample. Our approach obtains better performance than the state-of-the-art methods on ICDAR-2013 competition dataset. INDEX TERMS Character conceptual model, character recognition, concept learning, stroke extraction.

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Section: Comparison With Different Methodsmentioning

confidence: 99%

Recognition of Handwritten Chinese Characters Based on Concept Learning

Liang

Wang

et al. 2019

IEEE Access

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“…Later, further improvements have been achieved by normalizing the hand-path by picking out a specific number of points [5]. Deep learning-based techniques employ one dimensional convolutional neural networks to learn the features from image-based representations [9]- [13]. It must be also noted that some methods were concerned with recognizing words written on a touch pad using finger strokes [9]- [11], as opposed to in-air drawing as in robotic applications.…”

Section: In-air Handwritten Number Recognitionmentioning

confidence: 99%

“…These methods are either based on image representation of numbers and shapes [4]- [13] followed by shape matching or the path representation of hand movement [14]- [20] followed by shape matching. Some methods are based on deep learning for shape matching [9]- [13].…”

Section: Introductionmentioning

confidence: 99%

Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition

Alwaely

Abhayaratne

2019

IEEE Access

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This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of the eigenvectors of the normalized Laplacian of the graph adjacency matrix for forming the feature vectors. The method proposes to use the eigenvector corresponding to the lowest eigenvalue for formulating the feature vectors as it captures the details of the structure of the graph. The use of the proposed graph spectral domain representation has been demonstrated in an in-air hand-drawn number and symbol recognition applications. It has achieved average accuracy rates of 99.56% and 99.44%, for numbers and symbols, respectively, outperforming the existing methods for all datasets used. It also has the added benefits of fast real-time operation and invariance to rotation and flipping, making the recognition system robust to different writing and drawing variations.

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“…By generating a reduced version of the original training set by building prototypes, the NPC improves the accuracy and execution time when running instance-based classification algorithms. There are some variations of prototype algorithms (Chih- Cheng and Li, 2009;Liao and Vemuri, 2002;Qu et al, 2018;Silva Sun et al (2015) Attribute values are in the form of a hesitant fuzzy set Gao et al (2017), Ren et al (2017) Attribute values are in the form of a soft fuzzy rough set Sun and Ma (2016) Attribute values are in the form of a gray set Kou and Wu (2014) Attribute values are in the form of a linguistic term set Zhang et al (2018) The professional approach to EDM Combination with the eventcloud platform to increase efficiency Cano et al, 2017). Empirical studies (Liu et al, 2017;Hu and Tan, 2016;Elkano et al, 2018) are used to show some advantages of the NPC, such as accuracy, reduction capabilities and runtime.…”

Section: 3mentioning

confidence: 99%

Categories in emergency decision-making: prototype-based classification

Cai

Wei

Cao

2019

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Purpose This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security. Design/methodology/approach The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category. Findings An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given. Practical implications To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation. Originality/value The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.

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In-air handwritten Chinese character recognition with locality-sensitive sparse representation toward optimized prototype classifier

Cited by 28 publications

References 23 publications

Recognition of Handwritten Chinese Characters Based on Concept Learning

Recognition of Handwritten Chinese Characters Based on Concept Learning

Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition

Categories in emergency decision-making: prototype-based classification

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