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.   The proposed method is applied to a complex problem in historical document image analysis, where we show that is capable of robustly detecting text lines in the images from the ANDAR-TL competition. Experiments indicate that the model can cope with difficult situations and reach the state of the art in Vision such as other deep models.
['Bastien Moysset', 'Pierre Adam', 'Christian Wolf', 'Jérôme Louradour']
Space Displacement Localization Neural Networks to locate origin points of handwritten text lines in historical documents