In this work we propose a novel method for text spotting from scene images based on augmented Multi-resolution Maximally Stable Extremal Regions and Convolutional Neural Networks. The goal of this work is augmenting text character proposals to maximize their coverage rate over text elements in scene images, to obtain satisfying text detection rates without the need of using very deep architectures nor large amount of training data. Using simple and fast geometric transformations on multi-resolution proposals our system achieves good results for several challenging datasets while also being computationally efficient to train and test on a desktop computer.
|Titolo:||Augmented text character proposals and convolutional neural networks for text spotting from scene images|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||Relazione (in Volume)|