Space Displacement Localization Neural Networks to locate origin points of handwritten text lines in historical documents
Bastien Moysset, Pierre Adam, Christian Wolf, Jérôme Louradour · 2015
We describe a new method for detecting and localizing multiple objects in an image using context aware deep neural networks. Common architectures either proceed locally per pixel-wise sliding-windows, or globally by predicting object localizations for a full image. We improve on this by training a semi-local model to detect and localize objects inside a large image region, which covers an object or a part of it. Context knowledge is integrated, combining multiple predictions for different regions through a spatial context layer modeled as an LSTM network.