IDENTIFYING VERSIONS OF A FORM
Disclosed are a method and apparatus for identifying versions of a form. In an example, clients of a medical company fill out many forms, and many of these forms have multiple versions. The medical company operates in 10 states, and each state has a different version of a client intake form, as well as of an insurance identification form. In order to automatically extract information from a particular filled out form, it may be helpful to identify a particular form template, as well as the version of the form template, of which the filled out form is an instance. A computer system evaluates images of filled out forms, and identifies various form templates and versions of form templates based on the images.
1 . A method comprising:
receiving a plurality of images of forms;
initializing a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images;
aligning exemplar images from two clusters of the plurality of clusters based on keypoints of the exemplar images;
computing a form similarity score based on the alignment of the exemplar images;
when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; and
identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
2 . The method of claim 1 , further comprising:
determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and
assign all forms in each cluster to a particular template form based on said determining.
3 . The method of claim 1 , wherein the similarity score is based on a sub-comparisons of predetermined regions of each of the plurality of images, wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions.
4 . The method of claim 3 , wherein the predetermined regions are based on the keypoints in the plurality of images.
5 . The method of claim 1 , wherein said computing is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images.
6 . The method of claim 5 , wherein said computing further includes:
assigning a class label to each vector element in the respective image.
7 . The method of claim 5 , wherein said computing is performed using a trained machine learning model that receives vectors and outputs a confidence of similarity score.
8 . The method of claim 7 , wherein the confidence of similarity score is based on a “k-nearest neighbors” classification.
9 . A system comprising:
A processor:
A memory including instructions that when executed cause the processor to:
receive a plurality of images of forms;
initialize a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images;
align exemplar images from two clusters of the plurality of clusters based on keypoints of the exemplar images;
compute a form similarity score based on the alignment of the exemplar images;
when the form similarity score is above a predetermined threshold, merge the two clusters into a same cluster of the plurality of clusters; and
identify form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
10 . The system of claim 9 , wherein the instructions further comprise:
determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and
assign all forms in each cluster to a particular template form based on said determining.
11 . The system of claim 9 , wherein the similarity score is based on a sub-comparisons of predetermined regions of each of the plurality of images, wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions.
12 . The system of claim 11 , wherein the predetermined regions are based on the keypoints in the plurality of images.
13 . The system of claim 9 , wherein said compute instruction is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images.
14 . The system of claim 13 , wherein said compute instruction further includes:
assigning a class label to each vector element in the respective image.
15 . The system of claim 13 , wherein said compute instruction is performed using a trained machine learning model that receives vectors and outputs a confidence of similarity score.
16 . The system of claim 15 , wherein the confidence of similarity score is based on a “k-nearest neighbors” classification.
17 . One or more non-transitory computer-readable mediums having executable instructions stored thereon that, when executed by one or more processors, perform the operations of:
receiving a plurality of images of forms;
initializing a plurality of clusters based on the plurality of images, wherein each cluster of the plurality of clusters includes an exemplar image that is one of the plurality of images;
aligning exemplar images from two clusters of the plurality of clusters based on keypoints of the exemplar images;
computing a form similarity score based on the alignment of the exemplar images;
when the form similarity score is above a predetermined threshold, merging the two clusters into a same cluster of the plurality of clusters; and
identifying form images from one cluster of the plurality of clusters as being versions of a same template form based on the form images being in the one cluster.
18 . The one or more non-transitory computer-readable mediums of claim 17 , further comprising:
determining a single template form associated with each of the plurality of clusters via registration of a single image of each of the plurality of clusters of forms against a set template forms; and
assign all forms in each cluster to a particular template form based on said determining.
19 . The one or more non-transitory computer-readable mediums of claim 17 , wherein the similarity score is based on a sub-comparisons of predetermined regions of each of the plurality of images, wherein the predetermined regions are based on regions of a set of template forms that are most indicative of variations between a plurality of form versions.
20 . The one or more non-transitory computer-readable mediums of claim 19 , wherein the predetermined regions are based on the keypoints in the plurality of images.
21 . The one or more non-transitory computer-readable mediums of claim 17 , wherein said computing is based on a threshold of matching vector elements, wherein the vector elements describe each of the plurality of images.
22 . The one or more non-transitory computer-readable mediums of claim 21 , wherein said computing further includes:
assigning a class label to each vector element in the respective image.
23 . The one or more non-transitory computer-readable mediums of claim 21 , wherein said computing is performed using a trained machine learning model that receives vectors and outputs a confidence of similarity score.
24 . The one or more non-transitory computer-readable mediums of claim 23 , wherein the confidence of similarity score is based on a “k-nearest neighbors” classification.