By Topic

Decoding Substitution Ciphers by Means of Word Matching with Application to OCR

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Nagy, G. ; Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180. ; Seth, S. ; Einspahr, Kent

A substitution cipher consists of a block of natural language text where each letter of the alphabet has been replaced by a distinct symbol. As a problem in cryptography, the substitution cipher is of limited interest, but it has an important application in optical character recognition. Recent advances render it quite feasible to scan documents with a fairly complex layout and to classify (cluster) the printed characters into distinct groups according to their shape. However, given the immense variety of type styles and forms in current use, it is not possible to assign alphabetical identities to characters of arbitrary size and typeface. This gap can be bridged by solving the equivalent of a substitution cipher problem, thereby opening up the possibility of automatic translation of a scanned document into a standard character code, such as ASCII. Earlier methods relying on letter n-gram frequencies require a substantial amount of ciphertext for accurate n-gram estimates. A dictionary-based approach solves the problem using relatively small ciphertext samples and a dictionary of fewer than 500 words. Our heuristic backtrack algorithm typically visits only a few hundred among the 26! possible nodes on sample texts ranging from 100 to 600 words.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-9 ,  Issue: 5 )