Post-processing algorithms for the formation of online handwritten Gurmukhi character/akshara

2021 ◽  
pp. 1-11
Author(s):  
Harjeet Singh ◽  
R.K. Sharma ◽  
Muthukumaran Malarvel
Keyword(s):  
Cross Validation ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Hybrid Approach ◽  
Support Vector ◽  
Post Processing ◽  
Accuracy Rate ◽  
Online Handwriting Recognition ◽  
Recognition Systems ◽  
Processing Algorithms

Formation of Gurmukhi character/akshara from the recognized strokes in online handwriting recognition systems is a challenging task. In this paper, the task of character and akshara formation in an unconstrained environment have been addressed. After the recognition of online handwritten strokes the Gurmukhi akshara is formed using a hybrid approach. Two classifiers, namely, Support Vector Machine (SVM) and Recurrent Neural Network (RNN) have been experimented in this study. The classifier, yielded the maximum cross-validation accuracy has been utilized for stroke recognition. A total of 52,500 word samples have been collected from 175 writers in order to train the classifiers. Three post processing algorithms have been proposed in this article for improving the character and akshara recognition accuracy. The proposed methodology when tested on a dataset of 21,500 aksharas, written by 50 new writers, achieved average the accuracy rate of 97.1% and 87.1% for base character and akshara recognition, respectively.

New Feature Selection Method Based on SVM-RFE

2014 ◽  
Vol 926-930 ◽  
pp. 3100-3104 ◽  
Author(s):  
Xi Wang ◽  
Qiang Li ◽  
Zhi Hong Xie
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Recognition Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Testing Time ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Accuracy Rate ◽  
New Feature

This article analyzed the defects of SVM-RFE feature selection algorithm, put forward new feature selection method combined SVM-RFE and PCA. Firstly, get the best feature subset through the method of cross validation of k based on SVM-RFE. Then, the PCA decreased the dimension of the feature subset and got the independent feature subset. The independent feature subset was the training and testing subset of SVM. Make experiments on five subsets of UCI, the results indicated that the training and testing time was shortened and the recognition accuracy rate of the SVM was higher.

scholarly journals Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Young-Joo Han ◽  
Wooseong Kim ◽  
Joon-Sang Park
Keyword(s):  
Eye Tracking ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mobile Environments ◽  
Detection Problem ◽  
Resource Limited ◽  
Learning Techniques ◽  
Key Issues ◽  
Recognition Systems

We propose an efficient method that can be used for eye-blinking detection or eye tracking on smartphone platforms in this paper. Eye-blinking detection or eye-tracking algorithms have various applications in mobile environments, for example, a countermeasure against spoofing in face recognition systems. In resource limited smartphone environments, one of the key issues of the eye-blinking detection problem is its computational efficiency. To tackle the problem, we take a hybrid approach combining two machine learning techniques: SVM (support vector machine) and CNN (convolutional neural network) such that the eye-blinking detection can be performed efficiently and reliably on resource-limited smartphones. Experimental results on commodity smartphones show that our approach achieves a precision of 94.4% and a processing rate of 22 frames per second.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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