Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach

  • Authors:

  • Marisa Morita_aff1n2;Robert Sabourin_aff1n2n3;Flávio Bortolozzi;Y. Suen

  • Affiliations:

  • École de Technologie Supérieure, Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA), Montreal, Canada;École de Technologie Supérieure, Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA), Montreal, Canada;aff3 Pontíficia Universidade Católica do Paraná (PUCPR), Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA), Curitiba, Brazil;aff2 Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA), Montreal, Canada

  • Venue:
  • International Journal on Document Analysis and Recognition
  • Year:
  • 2003

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Abstract

This paper presents an HMM-MLP hybrid system for segmenting and recognizing complex date images written on Brazilian bank checks. Through the recognition process, the system makes use of an HMM-based approach to segment a date image into subfields. Then the three obligatory date subfields (day, month, and year) are processed. A neural approach has been adopted to decipher strings of digits (day and year) and a Markovian strategy to recognize and verify words (month). The final decision module makes an accept/reject decision. We also introduce the concept of metaclasses of digits to reduce the lexicon size of the day and year and improve the precision of their segmentation and recognition. Experiments show interesting results on date recognition.