Feedback Based Architecture for Reading Check Courtesy Amounts

In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either on intra-organization basis or on inter-organization basis, including paper intensive applications in the check processing application. In many countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. Such process of manual reading of each check involves significant time and cost. In this research, a new approach is introduced to read the numerical amount field on the check; also known as the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: segmentation, normalization, and the recognition of each character using a neural network classifier, with results better than many other methods in the literaratu

Handwritten Bank Check Recognition of Courtesy Amounts

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates

A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

The Cowl - v.78 - n.2 - Sep 12, 2013

The Cowl - student newspaper of Providence College. Volume 78 - No. 2 - September 12, 2013. 20 pages

The Cowl - v.79 - n.14 - Jan 22, 2015

The Cowl - student newspaper of Providence College. Vol 79 - No. 14 - January 22, 2015. 20 pages

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Adding feedback to improve segmentation and recognition of handwritten numerals

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Susan A. Dey.S.B.and M.Eng

The Cowl - v.81 - n.16 - Feb 9, 2017

The Cowl - student newspaper of Providence College. Volume 81, Number 16 - February 9, 2017. 28 pages

The Cowl - v.83 - n.9 - Nov 8, 2018

The Cowl - student newspaper of Providence College. Vol 83 - No. 9 - November 8, 2018. 28 pages