A System for Processing Handwritten Bank Checks Automatically
Pontifical University Comillas of Madrid
Pace University - The Seidenberg School of Computer Science and Information Systems
MIT Sloan Working Paper No. 4346-02; Eller College Working Paper No. 1028-05
In the US and many other countries, bank checks are preprinted with the account number and the check number in MICB ink and format; as such, these two numeric fields can be easily read and processed using automated techniques. However, the amount field on a filled -in check is usually read by human eyes, and involves significant time and cost, especially when one considers that about 68 billion checks are processed per annum in the US alone.
The system described in this paper uses the scanned image of a bank check to "read" the check. There are four main stages in the system that focus on: the detection of courtesy amount block within the image; the segmentation of string into characters; the recognition of isolated characters; and the post processing process that ensures correct recognition.
The detection of courtesy amounts is performed using heuristic rules. These rules are applied after processing the image to translate the set of gray pixels into groups of pixels, which are considered as strings. The segmentation of the courtesy amount is the most difficult part of the process because of the largely unconstrained nature of handwritten amounts on checks. The proposed segmentation has been implemented as a recursive process that interacts with the recognition module. The recognition of the isolated characters is based on an array of neural networks that has been demonstrated to be very accurate and computationally efficient. Finally, the post-processing module is used to minimize the incidence of incorrect readings by verifying that the sequence of numbers, periods, and commas matches the correct syntax for the recognized value of the check. Several of the algorithms included in the system are generalizable and can also be applied to address other applications involving reading of handwritten inputs.
Number of Pages in PDF File: 35
Keywords: Automated Check Reading, Automated Reading of Handwritten Information, Knowledge Acquisition
JEL Classification: M0, Z0
Date posted: March 19, 2002
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