5 Extraction of Common Feature of Dysgraphia Patients by Handwriting Analysis Using Variational Autoencoder
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Abstract
Nowadays, handwritten document analysis using intelligent computing technology is a demanding research area, considering its usefulness in identifying a person and human characteristics, particularly that of persons having typical disabilities such as dyslexia, dysgraphia, and Parkinson’s disease. Analysis of handwriting, falling under the broad purview of graphology, helps us understand the writer’s psychology, emotional outlays, and noticeable disorders as well. Since there prevails a broad spectrum of cursive nature and high inconsistency of handwriting styles, the techniques for modern handwriting analysis need to be more robust and sensitive to different patterns compared to the traditional graphological techniques. Herein lies the necessity of computing technology, which should intelligently analyze handwritten texts to find out the similarity of finer aspects of handwritings of children or adult with some kind of learning/writing disability. Deep learning technology is chosen as the technical tool to identify and classify common features of handwriting of children with developmental dysgraphia. Variational autoencoder, a deep unsupervised learning technique, is presently used for this purpose. This chapter reports successful extraction and interpretation of significant number of distinguishable handwriting characteristics that are clinically proved to be symptoms of dysgraphia.
Abstract
Nowadays, handwritten document analysis using intelligent computing technology is a demanding research area, considering its usefulness in identifying a person and human characteristics, particularly that of persons having typical disabilities such as dyslexia, dysgraphia, and Parkinson’s disease. Analysis of handwriting, falling under the broad purview of graphology, helps us understand the writer’s psychology, emotional outlays, and noticeable disorders as well. Since there prevails a broad spectrum of cursive nature and high inconsistency of handwriting styles, the techniques for modern handwriting analysis need to be more robust and sensitive to different patterns compared to the traditional graphological techniques. Herein lies the necessity of computing technology, which should intelligently analyze handwritten texts to find out the similarity of finer aspects of handwritings of children or adult with some kind of learning/writing disability. Deep learning technology is chosen as the technical tool to identify and classify common features of handwriting of children with developmental dysgraphia. Variational autoencoder, a deep unsupervised learning technique, is presently used for this purpose. This chapter reports successful extraction and interpretation of significant number of distinguishable handwriting characteristics that are clinically proved to be symptoms of dysgraphia.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of Contributors XIII
- 1 Deep Learning – A State-of-the-Art Approach to Artificial Intelligence 1
- 2 Convolutional Neural Networks: A Bottom-Up Approach 21
- 3 Handwritten Digit Recognition Using Convolutional Neural Networks 51
- 4 Impact of Deep Neural Learning on Artificial Intelligence Research 69
- 5 Extraction of Common Feature of Dysgraphia Patients by Handwriting Analysis Using Variational Autoencoder 85
- 6 Deep Learning for Audio Signal Classification 105
- 7 Backpropagation Through Time Algorithm in Temperature Prediction 137
- Index 153
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of Contributors XIII
- 1 Deep Learning – A State-of-the-Art Approach to Artificial Intelligence 1
- 2 Convolutional Neural Networks: A Bottom-Up Approach 21
- 3 Handwritten Digit Recognition Using Convolutional Neural Networks 51
- 4 Impact of Deep Neural Learning on Artificial Intelligence Research 69
- 5 Extraction of Common Feature of Dysgraphia Patients by Handwriting Analysis Using Variational Autoencoder 85
- 6 Deep Learning for Audio Signal Classification 105
- 7 Backpropagation Through Time Algorithm in Temperature Prediction 137
- Index 153
