LAP Lambert Academic Publishing Image and video text recognition using convolutional neural networks: Study of new CNNs architectures for binarization, segmentation and recognition of text images

Product Description Thanks to increasingly powerful storage media, multimedia resources have become nowadays essential resources and the challenge is how to quickly find relevant information. To accomplish this task, the text within images and videos can be a relevant key. In this work we focus on recognizing the content of the text and we assume that the text box has been detected and located correctly. We focused on a particular machine learning algorithm called convolutional neural networks (CNNs). These are networks of neurons whose topology is similar to the mammalian visual cortex. CNNs were initially used for recognition of handwritten digits. They were then applied successfully on many problems of pattern recognition. We propose in this work a new method of binarization of text images, a new method for segmentation of text images, the study of a convolutional neural network for character recognition in images, a discussion on the relevance of the binarization step in the recognition of text in images based on machine learning methods, and a new method of text recognition in images based on graph theory. About the Author Zohra Saidane received the telecommunications engineering degree from ENST Bretagne, Brest, France and Eurecom, Sophia Antipolis, France. Then the Master degree in computer science in 2005 from the University of Nice Sophia Antipolis. Then the PhD degree in computer vision from Telecom ParisTech in 2008.