Official logo recognition based on multilayer convolutional neural network model

Zahraa Najm Abdullah (University of Kerbala)
Zinah Abdulridha Abutiheen (University of Kerbala)
Ashwan A. Abdulmunem (University of Kerbala)
Zahraa A. Harjan (University of Kerbala)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 20, No 5: October 2022

Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recognize the logo with high accuracy rate about 99.16%. The proposed multi-layers CNN model proves the effectiveness to classify different logos with various conditions.