Word spotting for historical documents

By Toni M. Rath, Raghavan Manmatha. International Journal of Document Analysis and Recognition (IJDAR), 2007

300 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Lexicon-free handwritten word spotting using character HMMs (2012, methodology): “DTW-based keyword spotting was proposed in [28] for speech recognition and is also well-established in the field of handwritten word spotting [15, 16, 17, 18].”
Deep Features for Text Spotting (2014, background): “Authors have subsequently focused solely on text detection [7, 11, 16, 50, 51], or text recognition [31, 36, 41], or on combining both in end-to-end systems [40, 39, 49, 32–34, 45, 35, 6, 8, 48].”
Recognition and retrieval of mathematical expressions (2011, methodology): “Connected components in the query image are matched with connected components in archived images using visual similarity of connected components, again based on features similar to Rath and Manmatha's.”
Reading Text in the Wild with Convolutional Neural Networks (2014, methodology): “This is word detection – in an ideal scenario we would be able to generate word bounding boxes with high recall and high precision, achieving this by extracting the maximum amount of information from each bounding box candidate possible.”