A generic method of cleaning and enhancing handwritten data from business forms |
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Authors: | Xiangyun Ye Mohamed Cheriet Ching Y Suen |
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Affiliation: | (1) Centre for Pattern Recognition and Machine Intelligence, Concordia University, Suite GM606, 1455 de Maisonneuve Blvd. West, Montréal, Québec H3G 1M8, Canada, CA;(2) Laboratory for Imagery, Vision and Artificial Intelligence, école de Technologie Supérieure, University of Quebec, 1100, rue Notre-Dame Ouest, Montréal, Québec H3C 1K3, Canada, CA |
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Abstract: | The automation of business form processing is attracting intensive research interests due to its wide application and its
reduction of the heavy workload due to manual processing. Preparing clean and clear images for the recognition engines is
often taken for granted as a trivial task that requires little attention. In reality, handwritten data usually touch or cross
the preprinted form frames and texts, creating tremendous problems for the recognition engines. In this paper, we contribute
answers to two questions: “Why do we need cleaning and enhancement procedures in form processing systems?” and “How can we
clean and enhance the hand-filled items with easy implementation and high processing speed?” Here, we propose a generic system
including only cleaning and enhancing phases. In the cleaning phase, the system registers a template to the input form by
aligning corresponding landmarks. A unified morphological scheme is proposed to remove the form frames and restore the broken
handwriting from gray or binary images. When the handwriting is found touching or crossing preprinted texts, morphological
operations based on statistical features are used to clean it. In applications where a black-and-white scanning mode is adopted,
handwriting may contain broken or hollow strokes due to improper thresholding parameters. Therefore, we have designed a module
to enhance the image quality based on morphological operations. Subjective and objective evaluations have been studied to
show the effectiveness of the proposed procedures.
Received January 19, 2000 / Revised March 20, 2001 |
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Keywords: | : Form processing – Item extraction – Handwriting recognition – Goal-directed evaluation – Mathematical morphology |
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