Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning

• URL: http://arxiv.org/abs/2311.15728v1
• Date: Mon, 27 Nov 2023 11:26:41 GMT
• Title: Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning
• Authors: Michael Adjeisah, Kwame Omono Asamoah, Martha Asamoah Yeboah, Raji Rafiu King, Godwin Ferguson Achaab and Kingsley Adjei
• Abstract summary: We build a CNN model for classification and recognition using six convolutional layers, three fully connected layers, and optional dropout regularization. We assess the model's performance by measuring its accuracy and convergence rate. We hope this application inspires ideas on the various uses of AI in organizing our traditional and modern lives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) has emerged as a transformative influence, engendering paradigm shifts in global societies, spanning academia and industry. However, in light of these rapid advances, addressing the underrepresentation of black communities and African countries in AI is crucial. Boosting enthusiasm for AI can be effectively accomplished by showcasing straightforward applications around tasks like identifying and categorizing traditional symbols, such as Adinkra symbols, or familiar objects within the community. In this research endeavor, we dived into classical machine learning and harnessed the power of deep learning models to tackle the intricate task of classifying and recognizing Adinkra symbols. The idea led to a newly constructed ADINKRA dataset comprising 174,338 images meticulously organized into 62 distinct classes, each representing a singular and emblematic symbol. We constructed a CNN model for classification and recognition using six convolutional layers, three fully connected (FC) layers, and optional dropout regularization. The model is a simpler and smaller version of VGG, with fewer layers, smaller channel sizes, and a fixed kernel size. Additionally, we tap into the transfer learning capabilities provided by pre-trained models like VGG and ResNet. These models assist us in both classifying images and extracting features that can be used with classical machine learning models. We assess the model's performance by measuring its accuracy and convergence rate and visualizing the areas that significantly influence its predictions. These evaluations serve as a foundational benchmark for future assessments of the ADINKRA dataset. We hope this application exemplar inspires ideas on the various uses of AI in organizing our traditional and modern lives.

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