  Recent advances in segmentation-free keyword spotting treat this problem
w.r.t. an object detection paradigm and borrow from state-of-the-art detection
systems to simultaneously propose a word bounding box proposal mechanism and
compute a corresponding representation. Contrary to the norm of such methods
that rely on complex and large DNN models, we propose a novel segmentation-free
system that efficiently scans a document image to find rectangular areas that
include the query information. The underlying model is simple and compact,
predicting character occurrences over rectangular areas through an implicitly
learned scale map, trained on word-level annotated images. The proposed
document scanning is then performed using this character counting in a
cost-effective manner via integral images and binary search. Finally, the
retrieval similarity by character counting is refined by a pyramidal
representation and a CTC-based re-scoring algorithm, fully utilizing the
trained CNN model. Experimental validation on two widely-used datasets shows
that our method achieves state-of-the-art results outperforming the more
complex alternatives, despite the simplicity of the underlying model.
