Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks

Object classification, such as handwritten Arabic character recognition, is a computer vision application. Deep learning techniques such as convolutional neural networks (CNNs) are employed in character recognition to overcome the processing complexity with traditional methods. Usually, a CNN is followed by an activation function such as a rectified linear unit (ReLU) or leaky ReLU to filter the extracted features. Most handwritten character recognition endures an imbalanced number of positive and negative vectors. This issue decreases CNN performance when adopting ReLU and leaky ReLU for the next deep layers in the architecture. Hence, this study proposed an optimized leaky ReLU to retain more negative vectors using a CNN architecture with a batch normalization layer to address this weakness. To evaluate the proposed method, four datasets are used: Arabic Handwritten Characters Dataset (AHCD), self-collected, Modified National Institute of Standards and Technology (MNIST), and AlexU Isolated Alphabet (AIA9K). The proposed method shows significant performance in terms of accuracy, precision, and recall measures compared to the state-of-art methods. The results showed outstanding improvement over the known leaky ReLU as follows: 99% for AHCD, 95.4% for self-collected data, 90% for HIJJA dataset and 99% for Digit MNIST. The proposed CNN architecture with the proposed optimized leaky ReLU showed a stable accuracy performance and error rates between the training, validation, and testing phases. This indicates that most samples are trained and classified correctly.