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Recent advances in segmentation-free keyword spotting treat this problemw.r.t. an object detection paradigm and borrow from state-of-the-art detectionsystems to simultaneously propose a word bounding box proposal mechanism andcompute a corresponding representation. Contrary to the norm of such methodsthat rely on complex and large DNN models, we propose a novel segmentation-freesystem that efficiently scans a document image to find rectangular areas thatinclude the query information. The underlying model is simple and compact,predicting character occurrences over rectangular areas through an implicitlylearned scale map, trained on word-level annotated images. The proposeddocument scanning is then performed using this character counting in acost-effective manner via integral images and binary search. Finally, theretrieval similarity by character counting is refined by a pyramidalrepresentation and a CTC-based re-scoring algorithm, fully utilizing thetrained CNN model. Experimental validation on two widely-used datasets showsthat our method achieves state-of-the-art results outperforming the morecomplex alternatives, despite the simplicity of the underlying model.

Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring