Abstract: Identifying anchors for fields using optical character recognition data is described. A collection of characters is identified. The collection of characters includes a first set of characters at a first position relative to a first field in a first document and a second set of characters at a second position relative to the first field in the first document. The first set of characters is associated with a first word, and the second set of characters is associated with a second word. An anchor is created based on the collection of characters, wherein the anchor is at a third relative position to the first field in the first document. A second field is identified in a second document by identifying the anchor in the second document.

Abstract: Creating subgroups of documents using optical character recognition data is described. A matrix is created for words included in documents. Each column-row combination in the matrix indicates whether a corresponding word that is associated with the column-row combination is included in a corresponding document that is associated with the column-row combination. Distances are identified between pairs of the words. Each distance is based on a number of the documents that differ in including a corresponding pair of the words. Word clusters are created. Each word cluster includes pairs of words associated with a corresponding distance less than a distance threshold. Sets of word clusters are created. A set of word clusters includes word clusters that are not associated with any of the documents associated with other word clusters in the set. Subgroups of the digitized documents are created based on a set of word clusters with a highest word score.

Abstract: Using location transformations to identify objects is described. Word pairs are generated. Each word pair includes a first word from a first document and a corresponding second word from a second document. For each word pair, location information is computed for the words that indicates locations of the words in the documents relative to other words in the documents. A transformation is identified based on a comparison between the first and second location information. The transformation includes a translation, a rotation, and/or a scale. The transformation is applied to the second location information. A first anchor is identified in the first document if a difference between the first location information and the transformed second location information is less than a threshold value. A second anchor is identified in the second document based on the first anchor and the transformation.

Abstract: Using keywords to merge document clusters is described. Documents are distributed into document clusters that include a first document cluster of first documents and a second document cluster of second documents. A template associated with the first document cluster is created. The template includes keywords associated with most of the first documents. A distance is calculated between keyword location information associated with the template and word location information associated with a document in the second document cluster. The keyword location information includes information indicating a location of a keyword in the template relative to other keywords in the template. The word location information includes information indicating a location of a word in the document relative to other words in the document. A determination is made whether the distance is less than a threshold value.

Abstract: Classifying documents that have different scales is described. Instances are counted for each character size in documents. Character sizes for the first document and the second document are selected based on the instance count for each character size. Scales are calculated based on ratios of each first character size relative to each second character size. Scale products are calculated based on each instance count for each character size range for the first character sizes multiplied by each instance count for each corresponding character size range for the second character sizes. The corresponding character size range is based on a corresponding scale. Scale scores are calculated based on summing each of the scale products for each scale. A scale is selected based a highest scale score. The second document may be classified with the first document based on a comparison of first document location information and second document location information.

Abstract: A document template for classifying documents is created for each document class. The document template includes a set of keywords and the spatial relations of the keywords. A document to be classified is received. The spatial relations of the template keywords of a template are compared with the spatial relations of corresponding words in the document. If the spatial relations are the same, the document may be classified in the document class of the template.

Abstract: Document classes for classifying documents are created by comparing the spatial relations of words between a first and second document. If the spatial relations are the same, a document class may be created to classify documents similar to the first and second document. If the spatial relations are different, a first document class may be created to classify documents similar to the first document, and a second document class may be created to classify documents similar to the second document.

Abstract: The present invention relates to child-seat liners. Specifically, the present invention provides protective articles comprising child-seat liners, systems comprising child-seat liners, methods for using child-seat liners (e.g. with a shopping cart), and methods for making child-seat liners. The present invention also provides child-seat liner cut-pieces and patterns.