Keyword Spotting Simplified: A Segmentation-Free Approach using Character Counting and CTC re-scoring
This addresses the problem of efficient keyword spotting for document analysis, offering a simpler and more effective alternative to complex deep learning models.
The paper tackles keyword spotting in document images by proposing a segmentation-free approach using character counting and CTC re-scoring, achieving state-of-the-art results on two widely-used datasets.
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.