Adinkra Symbol Recognition using Classical Machine Learning and Deep
  Learning
Michael Adjeisah Kwame Omono Asamoah Martha Asamoah Yeboah Raji Rafiu King Godwin Ferguson Achaab Kingsley Adjei
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.