Recognition of Baybayin (Ancient Philippine Character) Handwritten Letters Using VGG16 Deep Convolutional Neural Network Model
International Journal of Emerging Trends in Engineering Research · 2020
We proposed a system that can convert 45 handwritten baybayin Philippine character/s into their corresponding Tagalog word/s equivalent through convolutional neural network (CNN) using Keras.The implemented architecture utilizes smaller, more compact type of VGG16 network.The classification used 1500 images from each 45 baybayin characters.The pixel values resulting from the resized characters (50x50 pixels) of the segmentation stage have been utilized for training the system, thus, achieving a 99.54% accuracy.To test the developed recognition system, 90 handwritten baybayin characters were captured in real time using its 1080P Full-HD web camera.Next, the system will classify the test sample.Lastly, the corresponding Tagalog word output is shown on the screen for the user.The overall accuracy for the testing phase is 98.84%.As a result, the proposed system will find possible applications in character extraction in documents and any related translation of handwritten document to structural text form.