  The current trend in object detection and localization is to learn
predictions with high capacity deep neural networks trained on a very large
amount of annotated data and using a high amount of processing power. In this
work, we propose a new neural model which directly predicts bounding box
coordinates. The particularity of our contribution lies in the local
computations of predictions with a new form of local parameter sharing which
keeps the overall amount of trainable parameters low. Key components of the
model are spatial 2D-LSTM recurrent layers which convey contextual information
between the regions of the image. We show that this model is more powerful than
the state of the art in applications where training data is not as abundant as
in the classical configuration of natural images and Imagenet/Pascal VOC tasks.
We particularly target the detection of text in document images, but our method
is not limited to this setting. The proposed model also facilitates the
detection of many objects in a single image and can deal with inputs of
variable sizes without resizing.
