Contextualizing noisy samples by substantially minimizing noise induced variance

Abstract

A system for contextualizing noisy samples by substantially minimizing noise induced variance may include a memory, an interface, and a processor. The memory is operative to store exemplars. The processor is operative to receive, via the interface, a sample which includes exemplar content corresponding to one of the exemplars, and noise. Variance induced by the noise may differentiate the sample from one or more of the exemplars. The processor may generalize the sample and the exemplars in order to substantially minimize the variance. The processor may compare the generalized sample to the generalized exemplars to identify the exemplar corresponding to the exemplar content of the sample. The processor may contextualize the sample based on a document type of the identified exemplar. The processor may present the contextualized sample to a user to facilitate interpretation thereof, and in response thereto, receive data representative of a user determination associated with the noise.

We claim: 
     
         1 . A method of contextualizing noisy samples comprising:
 receiving a sample comprising exemplar content and noise, wherein the exemplar content corresponds to one of a plurality of exemplars, and wherein variance induced by the noise differentiates the sample from one or more of the plurality of exemplars;   generalizing the sample and the plurality of exemplars in order to minimize the variance induced by the noise; and   comparing the generalized sample to each of the plurality of generalized exemplars to identify which of the plurality of exemplars corresponds to the exemplar content of the sample.   
     
     
         2 . The method of  claim 1  wherein the noise comprises an addition to the exemplar content, a distortion of the exemplar content, or a combination thereof. 
     
     
         3 . The method of  claim 2  wherein the noise comprises handwritten information, scanner skew, image noise, or a combination thereof. 
     
     
         4 . The method of  claim 1  further comprising contextualizing the sample based on a document type corresponding to the identified exemplar of the plurality of exemplars. 
     
     
         5 . The method of  claim 4  further comprising presenting the contextualized sample to a user to facilitate interpretation thereof and, in response thereto, receiving data representative of a user determination associated with the noise. 
     
     
         6 . The method of  claim 1  wherein the sample comprises an electronic document image, video, audio, or a combination thereof. 
     
     
         7 . The method of  claim 1  wherein the exemplar content comprises copyrighted content, and wherein the method further comprising identifying the sample as containing the copyrighted content. 
     
     
         8 . The method of  claim 1  wherein generalizing the sample and the plurality of exemplars comprises applying a moving average filter to the sample and the plurality of exemplars in order to minimize the variance induced by the noise. 
     
     
         9 . The method of  claim 1  further comprising providing a confidence indication which indicates a level of confidence that the sample corresponds to the one of the plurality of exemplars, wherein the level of confidence is based on a size of the noise relative to the size of the sample. 
     
     
         10 . A method of contextualizing noisy samples comprising:
 receiving a sample electronic document image comprising exemplar information corresponding to one of a plurality of exemplar electronic document images and random information, wherein variance induced by the random information renders the sample electronic document image distinguishable from one or more of the plurality of exemplar electronic document images;   applying a filter to the sample electronic document image and the plurality of exemplar electronic document images in order to minimize the variance induced by the random information of the sample electronic document image; and   identifying the one of the plurality of exemplar electronic document images corresponding to the exemplar information of the sample electronic document image by comparing the filtered sample electronic document image to each of the plurality of filtered exemplar electronic document images.   
     
     
         11 . The method of  claim 10  wherein the random information comprises an addition to the exemplar information, a distortion of the exemplar information, or a combination thereof. 
     
     
         12 . The method of  claim 10  further comprising contextualizing the sample electronic document image based on a document type corresponding to the identified exemplar electronic document image. 
     
     
         13 . The method of  claim 12  further comprising presenting the contextualized sample electronic document image to a user to facilitate interpretation thereof and, in response thereto, receiving data representative of a user determination associated with the random information. 
     
     
         14 . The method of  claim 10  wherein the filter comprises a moving average filter. 
     
     
         15 . The method of  claim 10  further comprising providing a confidence indication which indicates a level of confidence that the sample electronic document image corresponds to the one of the plurality of exemplar electronic document images, wherein the level of confidence is based on a size of the random information relative to the size of the sample. 
     
     
         16 . A method of contextualizing noisy samples comprising:
 receiving a sample electronic document image comprising exemplar content and noise;   generating a plurality of transformations of the sample electronic document image, wherein each of the plurality of transformations of the sample electronic document image corresponds to a different level of filtering of the exemplar content and the noise;   comparing the plurality of transformations of the sample electronic document image to a plurality of transformations of each of a plurality of exemplar electronic document images; and   determining one of the plurality of exemplar electronic document images of which a greatest number of the plurality of transformations satisfy a matching criteria with respect to the plurality of transformations of the sample electronic document image having a same level of filtering.   
     
     
         17 . The method of  claim 16  further comprising contextualizing the sample electronic document image based on a document type corresponding to the determined one of the plurality of exemplar electronic document images. 
     
     
         18 . The method of  claim 17  further comprising presenting the contextualized sample electronic document image to a user to facilitate interpretation thereof and, in response thereto, receiving data representative of a user determination associated with the noise. 
     
     
         19 . A method of classifying an electronic document image, the method comprising:
 receiving a sample electronic document image comprising content and noise;   filtering the sample electronic document image to account for variance induced by the noise;   filtering a plurality of exemplar electronic document images to a same level of filtering as the sample electronic document image; and   determining one of the plurality of exemplar electronic document images corresponding to the content of the sample electronic document image by comparing the filtered sample electronic document image to each of the plurality of filtered exemplar electronic document images.   
     
     
         20 . The method of  claim 19  further comprising contextualizing the sample electronic document based on a document type corresponding to the determined one of the plurality of exemplar electronic document images. 
     
     
         21 . The method of  claim 20  further comprising presenting the contextualized sample electronic document image to a user to facilitate interpretation thereof and, in response thereto, receiving data representative of a user determination associated with the noise. 
     
     
         22 . A method of classifying an electronic document image, the method comprising:
 identifying a plurality of exemplar electronic document images wherein each of the plurality of exemplar electronic document images is characterized by a document type;   receiving a sample electronic document image comprising a variation of one of the plurality of exemplar electronic document images, wherein variance induced by the variation distinguishes the sample electronic document image from one or more of the plurality of exemplar electronic document images;   filtering the sample electronic document image and the plurality of exemplar electronic document images in order to minimize the variance induced by the variation; and   comparing the filtered sample electronic document image to each of the plurality of filtered exemplar electronic document images to determine one of the plurality of exemplar electronic document images corresponding to the sample electronic document image.   
     
     
         23 . The method of  claim 22  further comprising contextualizing the sample electronic document based on the document type corresponding to the determined one of the plurality of exemplar electronic document images. 
     
     
         24 . The method of  claim 23  further comprising presenting the contextualized sample electronic document image to a user to facilitate interpretation thereof and, in response thereto, receiving data representative of a user determination associated with the variation. 
     
     
         25 . A system for contextualizing noisy samples comprising:
 a non-transitory memory configured to store a plurality of exemplars;   an interface coupled with the memory and configured to receive a sample comprising exemplar content corresponding to one of the plurality of exemplars and noise; and   a processor coupled with the interface and configured to receive, via the interface, the sample comprising the exemplar content corresponding to one of the plurality of exemplars and the noise, wherein variance induced by the noise differentiates the sample from one or more of the plurality of exemplars, generalize the sample and the plurality of exemplars to minimize the variance induced by the noise, and compare the generalized sample to each of the plurality of generalized exemplars to identify which of the plurality of exemplars corresponds to the exemplar content of the sample.