Abstract: This disclosure provides an exemplary method and system for extracting structured label and value pairwise textual data from a textual document. According to an exemplary method, initially a layout analysis is performed resulting in one or more alternatives for grouping and ordering the textual elements of interest. Next, textual elements are tagged as including a label term, a value term or a label and value term. Finally, a sequence-based method is applied to the tagged elements to generate one or more sequence listings representative of the label and value pairwise data structure(s) and label:value pairwise data is extracted.

Abstract: This disclosure provides an exemplary method and system for extracting structured label and value pairwise textual data from a textual document. According to an exemplary method, initially a layout analysis is performed resulting in one or more alternatives for grouping and ordering the textual elements of interest. Next, textual elements are tagged as including a label term, a value term or a label and value term. Finally, a sequence-based method is applied to the tagged elements to generate one or more sequence listings representative of the label and value pairwise data structure(s) and label:value pairwise data is extracted.

Abstract: A method and an apparatus for training a handwritten document categorizer are disclosed. For each category in a set into which handwritten documents are to be categorized, discriminative words are identified from the OCR output of a training set of typed documents labeled by category. A group of keywords is established including some of the discriminative words identified for each category. Samples of each of the keywords in the group are synthesized using a plurality of different type fonts. A keyword model is then generated for each keyword, parameters of the model being estimated, at least initially, based on features extracted from the synthesized samples. Keyword statistics for each of a set of scanned handwritten documents labeled by category are generated by applying the generated keyword models to word images extracted from the scanned handwritten documents. The categorizer is trained with the keyword statistics and respective handwritten document labels.

Abstract: A wordspotting system and method are disclosed. The method includes receiving a keyword and, for each of a set of typographical fonts, synthesizing a word image based on the keyword. A keyword model is trained based on the synthesized word images and the respective weights for each of the set of typographical fonts. Using the trained keyword model, handwritten word images of a collection of handwritten word images which match the keyword are identified. The weights allow a large set of fonts to be considered, with the weights indicating the relative relevance of each font for modeling a set of handwritten word images.

Abstract: A method for separating and categorizing documents includes receiving a scanned batch of documents. The batch includes scanned documents to which document separator stamps have been applied before scanning. Each stamp includes machine recognizable patterns applied on a same page of a document, spaced by a designated field for receiving a user-applied category code. The scanned batch of documents is processed to identify pages that contain a document separator, including identifying at least one of two spaced patterns. For a document page for which a document separator is identified, the the corresponding designated field is located and the category code associated with the designated field identified. The document containing the is separated from other documents in the batch based the identified separator and a document category is assigned to the document, based on the identified category code.

Abstract: A wordspotting system and method are disclosed. The method includes receiving a keyword and, for each of a set of typographical fonts, synthesizing a word image based on the keyword. A keyword model is trained based on the synthesized word images and the respective weights for each of the set of typographical fonts. Using the trained keyword model, handwritten word images of a collection of handwritten word images which match the keyword are identified. The weights allow a large set of fonts to be considered, with the weights indicating the relative relevance of each font for modeling a set of handwritten word images.

Abstract: A method, apparatus, and hardcopy document are provided. The method provides for separating and categorizing documents and includes receiving a scanned batch of documents. The batch includes a plurality of scanned documents to which document separator stamps have been applied before scanning. Each document separator stamp includes first and second machine recognizable patterns applied on a same page of a document, the first and second patterns being spaced by a designated field for receiving a user-applied category code. The scanned batch of documents is processed to identify pages that contain a document separator, the processing including identifying at least one of the first and second spaced patterns. For each of a plurality of document pages for which a document separator is identified, the method includes locating the corresponding designated field and identifying the category code associated with the designated field.

Abstract: A method and an apparatus for training a handwritten document categorizer are disclosed. For each category in a set into which handwritten documents are to be categorized, discriminative words are identified from the OCR output of a training set of typed documents labeled by category. A group of keywords is established including some of the discriminative words identified for each category. Samples of each of the keywords in the group are synthesized using a plurality of different type fonts. A keyword model is then generated for each keyword, parameters of the model being estimated, at least initially, based on features extracted from the synthesized samples. Keyword statistics for each of a set of scanned handwritten documents labeled by category are generated by applying the generated keyword models to word images extracted from the scanned handwritten documents. The categorizer is trained with the keyword statistics and respective handwritten document labels.