access icon free Scene text recognition by learning co-occurrence of strokes based on spatiality embedded dictionary

© The Institution of Engineering and Technology
Received 19/01/2014, Accepted 21/05/2014, Revised 20/05/2014, Published 16/06/2014

Text information contained in scene images is very helpful for high-level image understanding. In this study, the authors propose to learn co-occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids to incorporate spatial information, the authors SED associates every codeword with a particular response region and introduces more precise spatial information for robust character recognition. After localised soft coding and max pooling of the first layer, a sparse dictionary is learned to model co-occurrence of several local strokes, which further improves classification performance. Experimental results on two scene character recognition datasets ICDAR2003 and CHARS74 K demonstrate that their character recognition method outperforms state-of-the-art methods. Besides, competitive word recognition results are also reported for four benchmark word recognition datasets ICDAR2003, ICDAR2011, ICDAR2013 and street view text when combining their character recognition method with a conditional random field language model.

Inspec keywords: dictionaries; text detection; character recognition

Other keywords: max pooling; ICDAR2003 dataset; high-level image understanding; scene text recognition; sparse dictionary; spatiality embedded dictionary; local strokes; localised soft coding; robust character recognition; SED; text information; CHARS74 K dataset; scene images

Subjects: Image recognition; Computer vision and image processing techniques; Information analysis and indexing

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