Zoning Aggregated Hypercolumns for Keyword Spotting

Abstract

In this paper we present a novel descriptor and method for segmentation-based keyword spotting. We introduce Zoning-Aggregated Hypercolumn features as pixel-level cues for document images. Motivated by recent research in machine vision, we use an appropriately pretrained convolutional network as a feature extraction tool. The resulting local cues are subsequently aggregated to form word-level fixed-length descriptors. Encoding is computationally inexpensive and does not require learning a separate feature generative model, in contrast to other widely used encoding methods (such as Fisher Vectors). Keyword spotting trials on machine-printed and handwritten documents show that the proposed model gives very competitive results.