초록 ▼

There is described a system and method for automatically discriminating between different types of data with an image reader. In brief overview of one embodiment, the automatic discrimination feature of the present image reader allows a human operator to aim a hand-held image reader at a target that

There is described a system and method for automatically discriminating between different types of data with an image reader. In brief overview of one embodiment, the automatic discrimination feature of the present image reader allows a human operator to aim a hand-held image reader at a target that can contain a dataform and/or a signature and actuate the image reader. An autodiscrimination module in the image reader in one embodiment analyzes image data representative of the target and determines a type of data represented in the image data.

대표청구항 ▼

I claim: 1. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by searching the image data for an i

I claim: 1. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by searching the image data for an indication of a presence of a dataform; and if an indication of a dataform is present, attempting to decode the image data as dataform data; and if attempting to decode the image data as dataform data fails, testing the image data for characteristics indicative of a signature by performing a convolution step that employs one or more detector maps that are configured to indicate a presence of curved elements in order to identify textual features that indicate the presence of a signature; and outputting processed image data. 2. The method according to claim 1 wherein outputting processed image data comprises outputting decoded barcode data. 3. The method according to claim 1 wherein outputting processed image data comprises outputting signature data. 4. The method according to claim 1 wherein analyzing the image data and outputting processed image data comprise: outputting decoded dataform data if the attempt to decode the image data as dataform data is successful; and outputting signature data if the test for characteristics indicative of a signature is successful. 5. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by extracting one or more features of said image data by employing binarization, line thinning and convolution of said image data in search for indications of a presence of a signature; and outputting processed image data. 6. The method of claim 5 where said line thinning step includes transforming multi-pixel thick line segments into single pixel thick line segments. 7. The method of claim 5 where said binarization step includes transforming a grey scale level representation of said image data into a binary representation according to local thresholding and target image size normalization. 8. The method of claim 5 where said convolution step employs one or more detector maps that are configured to indicate a presence of curved elements in order to identify textual features that indicate the presence of a signature. 9. The method of claim 8 said convolution step employs a neural network to indicate a presence or absence of a signature. 10. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by searching the image data for markers that constitute an indication of a presence of a dataform; and if an indication of a dataform is present, attempting to decode the image data as dataform data; and if attempting to decode the image data as dataform data fails, testing the image data for characteristics indicative of a signature; and performing a binarization step and a convolution step to search for indications of a presence of a signature. 11. The method of claim 10 where said binarization step includes transforming a grey scale level representation of said image data into a binary representation according to local thresholding and target image size normalization, and where said binarization step is followed by a line thinning step that includes transforming multi-pixel thick line segments into single pixel thick line segments. 12. The method of claim 11 where said convolution step employs one or more detector maps that are configured to indicate a presence of curved elements in order to identify textual features that indicate the presence of a signature. 13. The method of claim 12 where said detector maps include 2횞3 binary curvelet information. 14. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by searching the image data for markers that constitute an indication of a presence of a dataform; and if an indication of a dataform is present, attempting to decode the image data as dataform data; and if attempting to decode the image data as dataform data fails, testing the image data for characteristics indicative of a signature; and performing a binarization step that includes transforming a grey scale level representation of said image data into a binary representation according to local thresholding and target image size normalization and performing a line thinning step that includes transforming multi-pixel thick line segments into single pixel thick line segments for the purpose of searching for indications of a presence of a signature. 15. The method of claim 14 where said line thinning step is followed by a convolution step that employs one or more detector maps that are configured to indicate a presence of curved elements in order to identify textual features that indicate the presence of a signature. 16. The method of claim 15 where said convolution step employs a neural network to indicate an orientation of a signature. 17. The method of claim 14 where histogram analysis is employed to indicate a boundary of a signature. 18. A method for automatically discriminating between a dataform and a signature, the method comprising: collecting image data from an object with an image reader; analyzing the image data to automatically discriminate between a dataform and a signature; by attempting to decode the dataform by executing one or more dataform decoding algorithms upon the dataform to yield a decoding result, said decoding result including decoded dataform data or an indication of a decoding error; and in response to if an indication of a dataform is present and to said decoding result, performing a binarization step that includes transforming a grey scale level representation of said image data into a binary representation according to local thresholding and target image size normalization; performing a line thinning step that includes transforming multi-pixel thick line segments into single pixel thick line segments; and extracting one or more features of said image data by convolution of said image data in search for indications of a presence of a signature. 19. The method of claim 18 where said convolution step employs detector maps that are configured to indicate a presence of curved elements. 20. The method of claim 18 where said convolution step employs a neural network to indicate an orientation of a signature. 21. The method of claim 18 where histogram analysis is employed to indicate a boundary of a signature. 22. The method of claim 18 where one-dimensional slices of pixels relative to an orientation of said signature are represented by said histogram analysis.