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EP0546843A2
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Image recognition method using finite state networks
Assignee: Xerox Corp
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Filing: 1992-12-10
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Granted: —
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Expires: 2012-12-10
Abstract
Abstract An image recognition system, in particular for document image recognition, using an imaging model employing a 2dimensional finite state automaton corresponding to a regular string grammar. This approach is not only less computationally intensive than previous grammar-based approaches to document image recognition, but also can handle a wider variety of image types. Features of the imaging model include a sidebearing model of glyph positioning, an image decoder based on linear scheduling theory for regular interactive algorithms, the combining of overlapping image sub-regions, and a least-squares estimation procedure for measuring character parameters from character samples in the image.
Inventors
- 👤 Gary Edward Kopec
- 👤 Philip Andrew Chou
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-10-29 19:47
CPC Classifications
G; G06; G06V; G06V30/00; G06V30/40; G06V30/41; G06V30/414; G06F; G06F18/00; G06F18/20; G06F18/29; G06F18/295; G06V30/10; G06V30/14; G06V30/148; G06V30/19; G06V30/191; G06V30/19187
Claims Analysis
Total Claims: 21
Claim 1
Independent Claim
69 words
A decoder for reconstructing the object used to make a bitmap image in an image recognition system, said decoder including a library of symbol templates substantially corresponding to the symbols present in the object and a finite state network for parsing the bitmap image to reconstruct the object by combining symbol templates selected from the library of templates on the basis of paths determined by the finite state network.
Claim 2
Dependent Claim
69 words
A decoder for reconstructing the object used to make a bitmap image in an image recognition system, said decoder including a library of symbol templates substantially corresponding to the symbols present in the object and a finite state network for parsing the bitmap image to reconstruct the object by combining symbol templates selected from the library of templates on the basis of paths determined by the finite state network.
Claim 3
Dependent Claim
28 words
The decoder of claim 1, wherein the finite state network comprises branches having a set of attributes including a label, an image displacement, and a node transition probability.
Claim 4
Dependent Claim
25 words
The decoder of claim 1, wherein the decoder further comprises a scheduler for ordering the computations for determining the most likely path through the network.
Claim 5
Dependent Claim
38 words
The decoder of claim 1, wherein the network comprises a Markov source in which each transition is labelled with a symbol template and a displacement and the symbol templates are based on a sidebearing model of the symbol.
Claim 6
Independent Claim
78 words
A method of decoding a bitmap image to be reconstructed in an image recognition system, comprising: inputting the bitmap image to a decoder, inputting a finite state network for the class of objects represented by the image, providing a library of symbol templates substantially corresponding to object symbols of the image, and causing said decoder to reconstruct the object by combining symbol templates selected from the library on the basis of paths determined by the finite state network.
Claim 7
Independent Claim
78 words
A method of decoding a bitmap image to be reconstructed in an image recognition system, comprising: inputting the bitmap image to a decoder, inputting a finite state network for the class of objects represented by the image, providing a library of symbol templates substantially corresponding to object symbols of the image, and causing said decoder to reconstruct the object by combining symbol templates selected from the library on the basis of paths determined by the finite state network.
Claim 8
Dependent Claim
20 words
The method of claim 5, wherein the symbol templates are derived from a sidebearing model of symbol description and positioning.
Claim 9
Dependent Claim
27 words
The method of claim 5, wherein the symbol templates are derived from images containing samples of the symbols by estimating the font metrics using an estimation procedure.
Claim 10
Independent Claim
100 words
A method of decoding a bitmap image to be reconstructed in an image recognition system using a finite state model of image generation for generating from a document comprising a plurality of characters a binary image from which the document can be intelligently reconstructed, comprising: (a) determining glyph positioning in the bitmap by determining the sidebearing attributes of the glyph, (b) producing character templates representing each glyph positioned as determined in step (a), (c) parsing the binary image using a selection of the templates produced by step (b) following the paths of a finite state network to reconstruct the document.
Claim 11
Independent Claim
100 words
A method of decoding a bitmap image to be reconstructed in an image recognition system using a finite state model of image generation for generating from a document comprising a plurality of characters a binary image from which the document can be intelligently reconstructed, comprising: (a) determining glyph positioning in the bitmap by determining the sidebearing attributes of the glyph, (b) producing character templates representing each glyph positioned as determined in step (a), (c) parsing the binary image using a selection of the templates produced by step (b) following the paths of a finite state network to reconstruct the document.
Claim 12
Dependent Claim
58 words
An imager, for an image recognition system, based on an imaging model comprising finite state networks and means for inputting an object to be imaged, said imager comprising means for constructing a bitmap image of the object by combining symbol templates selected from a library of templates on the basis of paths determined by a finite state network.
Claim 13
Dependent Claim
58 words
An imager, for an image recognition system, based on an imaging model comprising finite state networks and means for inputting an object to be imaged, said imager comprising means for constructing a bitmap image of the object by combining symbol templates selected from a library of templates on the basis of paths determined by a finite state network.
Claim 14
Dependent Claim
27 words
The imager of claim 9, wherein the finite state network comprises branches having a set of attributes including a label and a multidimensional displacement on the bitmap.
Claim 15
Dependent Claim
64 words
An image synthesis method, comprising: inputting an object to be imaged to an imager, inputting a finite state network for the class of objects to be imaged, providing a library of symbol templates, and causing said imager to construct a bitmap image of the object by combining symbol templates selected from the library on the basis of paths determined by the finite state network.
Claim 16
Dependent Claim
64 words
An image synthesis method, comprising: inputting an object to be imaged to an imager, inputting a finite state network for the class of objects to be imaged, providing a library of symbol templates, and causing said imager to construct a bitmap image of the object by combining symbol templates selected from the library on the basis of paths determined by the finite state network.
Claim 17
Dependent Claim
20 words
The method of claim 11, wherein the symbol templates are derived from a sidebearing model of symbol description and positioning.
Claim 18
Dependent Claim
27 words
The method of claim 11, wherein the symbol templates are derived from images containing samples of the symbols by estimating the font metrics using an estimation procedure.
Claim 19
Dependent Claim
17 words
An image recognition system comprising the combination of the imagerofclaim 9 and the decoder of claim 1.
Claim 20
Dependent Claim
76 words
An image recognition method comprising a finite state model of image generation for generating from a document comprising a plurality of characters a binary image from which the document can be intelligently reconstructed, comprising: (a) establishing character models from character samples used in the document by estimating the font metrics of each character using a least-squares procedure, (b) producing a template of each document character and using same and a Markov source to construct the image.
Claim 21
Dependent Claim
76 words
An image recognition method comprising a finite state model of image generation for generating from a document comprising a plurality of characters a binary image from which the document can be intelligently reconstructed, comprising: (a) establishing character models from character samples used in the document by estimating the font metrics of each character using a least-squares procedure, (b) producing a template of each document character and using same and a Markov source to construct the image.
References Cited (Backward Citations)
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