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After executing the experimental work, their comparison and analysis, it is concluded that the Random Forest classifier is performing better than other recently used classifiers for character and numeral recognition of offline handwritten Gurmukhi characters and numerals with the recognition accuracy of 87.9% for 13,000 samples.Īs the years passed by, computers became more powerful and automation became the need of generation. Based on the experimental results, it is clear that classifiers considered in this study have complementary rewards and they should be implemented in a hybrid manner to achieve higher accuracy rates. The paper also highlights the comparison of correctness of tests obtained by applying the selected classifiers. The performance is assessed by considering various parameters such as accuracy rate, size of the dataset, time taken to train the model, false acceptance rate, false rejection rate and area under receiver operating characteristic Curve. To assess the performance of classifiers, authors have used the Waikato Environment for Knowledge Analysis which is an open source tool for machine learning. For the experimental work, authors used a balanced data set of 13,000 samples that includes 7000 characters and 6000 numerals.
Various classifiers used and evaluated in this study include k-nearest neighbors, linear-support vector machine (SVM), RBF-SVM, Naive Bayes, decision tree, convolution neural network and random forest classifier.
This paper presents a comparative study of various classifiers and the results achieved for offline handwritten Gurmukhi characters and numerals recognition. Based on the learning adaptability and capability to solve complex computations, classifiers are always the best suited for the pattern recognition problems. The problem of classification is considered as one of the important problems for the development of applications and for efficient data analysis. In addition, this study has resulted in building a dataset of 17052 Sinhala character images.Ĭlassification is a process to pull out patterns from a number of classes by using various statistical properties and artificial intelligence techniques. Further, this study has analyzed the performance of the different type of features and identified the best performing features.
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This study has considered the full Sinhala alphabet of more than 600 characters and achieved high recognition accuracy. The overall recognition rate was observed to be 78.41%. Several trials were conducted to evaluate the performance of the proposed model with 203 unique character classes, where each class contains 14 font styles. Basic, density, a histogram of gradients (HoG) and Transition features were utilised to construct the decision tree. The DAG was implemented using OVO-based Support Vector Machines (SVMs) and UDT was implemented using OVA-based SVMs with RBF kernel. The decision tree is composed of a directed acyclic graph (DAG) followed by an unbalanced decision tree (UDT). This paper proposes a multi-class classification approach to recognize Sinhala printed characters using a hybrid decision tree. Printed Sinhala character recognition is a very challenging task, due to large number of complex structured characters and similarity between characters. However, research on recognizing printed Sinhala characters is in need of improvement, especially in terms of accuracy of recognition as well as completeness. Printed character recognition methods have reached very high performance for many languages. Printed character recognition is a well-researched area due to its necessity in many real-world applications.