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Reading cursive handwriting by alignment of letter prototypes

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Abstract

We describe a new approach to the visual recognition of cursive handwriting. An effort is made to attain human-like performance by using a method based on pictorial alignment and on a model of the process of handwriting. The alignment approach permits recognition of character instances that appear embedded in connected strings. A system embodying this approach has been implemented and tested on five different word sets. The performance was stable both across words and across writers. The system exhibited a substantial ability to interpret cursive connected strings without recourse to lexical knowledge.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computer Science, The Weizmann Institute of Science, 76100, Rehovot, Israel

    Shimon Edelman & Tamar Flash

  2. Department of Applied Mathematics and Computer Science, The Weizmann Institute of Science, 76100, Rehovot, Israel

    Shimon Ullman

  3. Department of Brain and Cognitive Sciences, MIT, 02139, Cambridge, MA, USA

    Shimon Ullman

  4. the Artificial Intelligence Laboratory, MIT, 02139, Cambridge, MA, USA

    Shimon Ullman

Authors
  1. Shimon Edelman
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  2. Tamar Flash
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  3. Shimon Ullman
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Additional information

SU is partially supported by NSF grant IRI-8900267.

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Edelman, S., Flash, T. & Ullman, S. Reading cursive handwriting by alignment of letter prototypes. Int J Comput Vision 5, 303–331 (1990). https://doi.org/10.1007/BF00126503

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