• DocumentCode
    1580072
  • Title

    Individuality of handwriting: a validation study

  • Author

    Srihari, Sargur N. ; Cha, Sung-Hyuk ; Arora, Hina ; Lee, Sangjik

  • Author_Institution
    Center of Excellence for Document Anal. & Recognition, State Univ. of New York, Buffalo, NY, USA
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    106
  • Lastpage
    109
  • Abstract
    Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of 1500 individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the expert document examiner
  • Keywords
    document image processing; feature extraction; handwritten character recognition; learning (artificial intelligence); US population; United States courts; age; character shapes; ethnic groups; expert testimony; feature extraction; gender; global attributes; handwriting individuality; line separation; machine learning approaches; scanned images; slant; validation study; Algorithm design and analysis; Feature extraction; Forensics; Handwriting recognition; Image analysis; Machine learning algorithms; Shape; Testing; Text analysis; Writing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7695-1263-1
  • Type

    conf

  • DOI
    10.1109/ICDAR.2001.953764
  • Filename
    953764