Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

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A feature extraction technique for online handwriting recognition

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) ◽

10.1109/ijcnn.2004.1380140 ◽

2005 ◽

Cited By ~ 10

Author(s):

B. Verma ◽

J. Lu ◽

M. Ghosh ◽

R. Ghosh

Keyword(s):

Feature Extraction ◽

Handwriting Recognition ◽

Extraction Technique ◽

Online Handwriting Recognition ◽

Feature Extraction Technique ◽

Online Handwriting

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DS-kNN

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2021040107 ◽

2021 ◽

Vol 15 (2) ◽

pp. 131-144

Author(s):

Redha Taguelmimt ◽

Rachid Beghdad

Keyword(s):

Intrusion Detection ◽

False Positive ◽

Detection Rate ◽

Nearest Neighbors ◽

The Other ◽

Intrusion Detection Systems ◽

K Nearest Neighbors ◽

Detection Systems ◽

Knn Classifier ◽

Better Than

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

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