HaarCNN: Detection and Recognition of Comic Strip Characters with Deep Learning and Cascade Classifiers
B.M. Kotlicki (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Y. Chen – Mentor (TU Delft - Data-Intensive Systems)
Zilong Zhao – Mentor (TU Delft - Data-Intensive Systems)
Arie Van Deursen – Graduation committee member (TU Delft - Software Technology)
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Abstract
Object detection and recognition is a computer vision problem tackled with techniques such as convolutional neural networks or cascade classifiers. This paper tackles the challenge of using the similar methods in the realm of comics strips characters. We approached the idea of combining cascade classifiers with various convolutional neural network architectures for character detection and recognition in consideration of maintaining low computational overhead. The alternative with the selective search algorithm step was also explored. The name of the pipeline is HaarCNN. We compared it to standard methods to verify a potential improvement. We evaluate 750 number of images extracted from comic strips and achieve over 85% precision and around 80% recall of detected faces and over 80% of correct main character recognitions. The images were processed in around 200 seconds. The potentially satisfying results of character annotation can be advantageous in deep learning sub-fields such as generative adversarial networks.