Work

A fast and robust text spotter

Year: 2016

Type: conference-paper

Abstract: We introduce an algorithm for text detection and localization ("spotting") that is computationally efficient and produces state-of-the-art results. Our system uses multi-channel MSERs to detect a large number of promising regions, then subsamples these regions using a clustering approach. Representatives of region clusters are binarized and then passed on to a deep network. A final line grouping stage forms word-level segments. On the ICDAR 2011 and 2015 benchmarks, our algorithm obtains an F-score of 82% and 83%, respectively, at a computational cost of 1.2 seconds per frame. We also introduce a version that is three times as fast, with only a slight reduction in performance.

Source: IEEE Winter Conference on Applications of Computer Vision

DOI: 10.1109/wacv.2016.7477663

Authors: Siyang Qin, Roberto Manduchi

Institution: University of California, Santa Cruz

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Eye Institute

Award: Enabling access to printed text for blind people via assisted mobile OCR