Handwritten Devanagari Character Classification Using CNN

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

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Building Dataset and Deep Learning-Based Inception Model for the Character Classification of Tigalari Script

10.1007/978-981-16-3342-3_20 ◽

2021 ◽

pp. 239-252

Author(s):

Sachin S. Bhat ◽

Alaka Ananth ◽

Rajashree Nambiar ◽

Nagaraj Bhat

Keyword(s):

Deep Learning ◽

Character Classification

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Offline handwritten character classification of the same scriptural family languages by using transfer learning techniques

2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) ◽

10.1109/icetce48199.2020.9091744 ◽

2020 ◽

Author(s):

Satyasangram Sahoo ◽

Prem Kumar B. ◽

R. Lakshmi

Keyword(s):

Transfer Learning ◽

Handwritten Character ◽

Learning Techniques ◽

Character Classification

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A Fuzzy Matching based Image Classification System for Printed and Handwritten Text Documents

Journal of Information Technology Research ◽

10.4018/jitr.2020040110 ◽

2020 ◽

Vol 13 (2) ◽

pp. 155-194

Author(s):

Shalini Puri ◽

Satya Prakash Singh

Keyword(s):

Image Classification ◽

Classification System ◽

Word Association ◽

Computation Time ◽

Document Classification ◽

Efficient Manner ◽

Text Documents ◽

Handwritten Text ◽

Fuzzy Matching ◽

Character Classification

This article proposes a bi-leveled image classification system to classify printed and handwritten English documents into mutually exclusive predefined categories. The proposed system follows the steps of preprocessing, segmentation, feature extraction, and SVM based character classification at level 1, and word association and fuzzy matching based document classification at level 2. The system architecture and its modular structure discuss various task stages and their functionalities. Further, a case study on document classification is discussed to show the internal score computations of words and keywords with fuzzy matching. The experiments on proposed system illustrate that the system achieves promising results in the time-efficient manner and achieves better accuracy with less computation time for printed documents than handwritten ones. Finally, the performance of the proposed system is compared with the existing systems and it is observed that proposed system performs better than many other systems.

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