Scanned compound document encoding using multiscale recurrent patterns

  • Authors:

  • Nelson C. Francisco;Nuno M. M. Rodrigues;Eduardo A. B. da Silva;Murilo Bresciani de Carvalho;Sérgio M. M. de Faria;Vitor M. M. Silva

  • Affiliations:

  • Instituto de Telecomunicações, Leiria, Portugal and PEE, COPPE, Universidade Federale Rio de Janeiro, Rio de Janeiro, Brazil;Instituto de Telecomunicações, Portugal and ESTG, Institute Polytechnic Leiria, Leiria, Portugal;PEE, COPPE, DEL, Poli, Universidade Federale Rio de Janeiro, Rio de Janeiro, Brazil;TET, CTC, Universidade Federale Fluminense, Niterói, Brazil;Instituto de Telecomunicações, Portugal and ESTG, Institute Polytechnic Leiria, Leiria, Portugal;Instituto de Telecomunicações and Dep. de Engenharia Electrotécnica e de Computadores, University of Coimbra-Pólo II, Coimbra, Portugal

  • Venue:
  • IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
  • Year:
  • 2010

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Abstract

In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input image's characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types.