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US5475768A
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High accuracy optical character recognition using neural networks with centroid dithering
Assignee: CANNON RESEARCH CENTER AMERICA Inc
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Filing: 1993-04-29
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Granted: 1995-12-12
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Expires: 2013-04-29
Abstract
Abstract Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.
Inventors
- 👤 Thanh A. Diep
- 👤 Hadar I. Avi-Itzhak
- 👤 Harry T. Garland
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-25 07:23
CPC Classifications
G; G06; G06V; G06V10/00; G06V10/20; G06F; G06F18/00; G06F18/20; G06F18/21; G06V10/70
Claims Analysis
Total Claims: 6
Claim 1
Independent Claim
156 words
A computer-implemented process of recognizing a pattern in an image among a set of known templates, the process comprising: a) training a neural network using said set of known templates; b) scanning said image; c) detecting said pattern by segmenting said scanned image into a detected pattern comprising a plurality of pixels, each such pixel having a value; d) preprocessing said detected pattern by: i) determining a minimum of said values of said pixels; ii) subtracting the minimum from said values of said pixels; producing thereby a corrected value for each such pixel; and iii) filtering said corrected pixel values by selectively assigning a predetermined filtered pixel value to a subset of said pixels responsive to said corrected values of said pixels in said subset not exceeding a threshold value; and e) recognizing said preprocessed detected pattern as corresponding to one of said known templates by applying said preprocessed detected pattern to said trained neural network.
Claim 2
Dependent Claim
156 words
A computer-implemented process of recognizing a pattern in an image among a set of known templates, the process comprising: a) training a neural network using said set of known templates; b) scanning said image; c) detecting said pattern by segmenting said scanned image into a detected pattern comprising a plurality of pixels, each such pixel having a value; d) preprocessing said detected pattern by: i) determining a minimum of said values of said pixels; ii) subtracting the minimum from said values of said pixels; producing thereby a corrected value for each such pixel; and iii) filtering said corrected pixel values by selectively assigning a predetermined filtered pixel value to a subset of said pixels responsive to said corrected values of said pixels in said subset not exceeding a threshold value; and e) recognizing said preprocessed detected pattern as corresponding to one of said known templates by applying said preprocessed detected pattern to said trained neural network.
Claim 3
Dependent Claim
142 words
A computer-implemented process of recognizing a pattern in an image among a set of known templates, the process comprising: a) training a neural network using said set of known templates; b) scanning said image; c) detecting said pattern by segmenting said scanned image into a detected pattern represented as a map of pixels, each having a multi-bit pixel value; d) preprocessing said detected pattern by: i) determining complements of each of said pixel values; ii) selecting as a noise value a minimum of said complements; iii) subtracting from each of said complements said noise value; and iv) selectively replacing said complements with a baseline value in response to said complements not exceeding a predetermined threshold value; and e) recognizing said preprocessed detected pattern as corresponding to one of said known templates by applying said preprocessed detected pattern to said trained neural network.
Claim 4
Dependent Claim
142 words
A computer-implemented process of recognizing a pattern in an image among a set of known templates, the process comprising: a) training a neural network using said set of known templates; b) scanning said image; c) detecting said pattern by segmenting said scanned image into a detected pattern represented as a map of pixels, each having a multi-bit pixel value; d) preprocessing said detected pattern by: i) determining complements of each of said pixel values; ii) selecting as a noise value a minimum of said complements; iii) subtracting from each of said complements said noise value; and iv) selectively replacing said complements with a baseline value in response to said complements not exceeding a predetermined threshold value; and e) recognizing said preprocessed detected pattern as corresponding to one of said known templates by applying said preprocessed detected pattern to said trained neural network.
Claim 5
Dependent Claim
140 words
Apparatus for recognizing a pattern in an image among a set of known templates, the apparatus comprising: scanning means for scanning said image; a preprocessor adapted to accept as input an image signal produced by said scanning means and producing therefrom a preprocessed pattern signal, said preprocessed pattern signal including a plural number of multibit pixel values, said preprocessor including an inverter for determining complements of said pixel values, a noise filter for subtracting from said complements a minimum value thereof to obtain corrected complements, said noise filter further selectively replacing said corrected complements with a baseline value in response to said corrected complements not exceeding a predetermined threshold value; and a neural network adapted to accept as input said preprocessed pattern signal and to produce therefrom an output signal indicative of said pattern corresponding to one of said templates.
Claim 6
Dependent Claim
140 words
Apparatus for recognizing a pattern in an image among a set of known templates, the apparatus comprising: scanning means for scanning said image; a preprocessor adapted to accept as input an image signal produced by said scanning means and producing therefrom a preprocessed pattern signal, said preprocessed pattern signal including a plural number of multibit pixel values, said preprocessor including an inverter for determining complements of said pixel values, a noise filter for subtracting from said complements a minimum value thereof to obtain corrected complements, said noise filter further selectively replacing said corrected complements with a baseline value in response to said corrected complements not exceeding a predetermined threshold value; and a neural network adapted to accept as input said preprocessed pattern signal and to produce therefrom an output signal indicative of said pattern corresponding to one of said templates.
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USPTO Annuity & Maintenance Windows
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| 3.5 Years (Year 4) | 1998-12-11 | 1999-06-12 | 1999-12-12 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2002-12-11 | 2003-06-12 | 2003-12-12 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2006-12-11 | 2007-06-12 | 2007-12-12 | $7700.00 | Window Closed / Lapsed |