Abstract: Methods and systems for transforming at least a portion of a physical document into digital data. One method includes obtaining a first plurality of data items automatically extracted from a first physical document and a validated value for a data field. The method also includes automatically identifying a first linked data item included in the first plurality of data items that is linked to the validated value and setting a physical position included in a rule to the physical position of the first linked data item. In addition, the method includes obtaining a second plurality of data items automatically extracted from a second physical document and automatically identifying a candidate data item included in the second plurality of data items based on the rule. Furthermore, the method includes automatically populating a value for the data field for the second physical document based on the candidate data item.

Abstract: A system for separating documents in a batch of unseparated documents. In one example, the system comprises a scanner, a display, and an electronic processor. In another example, the system comprises an electronic source, a display, and an electronic processor. The electronic processor is configured to receive, as input, a batch of unseparated documents and apply, image processing to each page in the batch. The electronic processor is also configured to determine, for each pair of consecutive pages in the batch of documents, a probability that pages of the pair of consecutive pages belong to different documents using a predictive model. The electronic processor is further configured to generate a batch of separated documents by providing an indication of a document boundary if the probability generated by the predictive model is above a predetermined threshold.

Abstract: Methods and systems for transforming at least a portion of a physical document into digital data. One method includes obtaining a first plurality of data items automatically extracted from a first physical document and a validated value for a data field. The method also includes automatically identifying a first linked data item included in the first plurality of data items that is linked to the validated value and setting a physical position included in a rule to the physical position of the first linked data item. In addition, the method includes obtaining a second plurality of data items automatically extracted from a second physical document and automatically identifying a candidate data item included in the second plurality of data items based on the rule. Furthermore, the method includes automatically populating a value for the data field for the second physical document based on the candidate data item.

Abstract: Systems and methods for automated document separation. The system includes a host device that is configured to communicate with one or more client devices over a network. The host device includes a splitting module, a correction module, a knowledge base, and a document store. The splitting module is configured to perform a multi-level document splitting. Pages are grouped into documents based on the sender information, the lack of sender information, and whether the sender is known. The splitting module performs an automatic correction of the initial document separation based on information stored within the knowledge base. The knowledge base is updated each time a document is processed and a user provides feedback related to whether the documents were successfully separated. Based on the success or failure of a particular document separation, the knowledge base evaluates the modifications made by a user to learn from the errors made during document separation.

Abstract: Embodiments of the invention relate to a software-as-a-service (“SaaS”) digital document management system (“DDMS”). The DDMS is located remotely from an office building or other place of business on a remote server. The server is connected to one or more databases for storing digital documents or information extracted from the digital documents. A user populates the DDMS by sending digital documents to the server using a computer, an email-enabled scanner, or the like. When a digital document is received at the server, the document is routed to a customer pending queue, information from the digital document is extracted, the digital document is stored in a directory, and the extracted information is stored in a relational database. The extracted information is supplemented or corrected as necessary using a web portal which allows a user to access the digital document and the extracted information.

Abstract: Systems and methods of recognizing a business document and creating a document signature. In one embodiment, a business document is scanned and a business document image is created. The business document image is compared to a template database. If a matching template is found, document fields are defined and extracted. If no matching document template is found, the document image is compared to a skeleton database. If a matching document skeleton in found, document fields are defined and extracted. A document skeleton is generated and then stored in the template database. If no matching document skeletons are found in the skeleton database, document fields are manually extracted. A document skeleton is then generated from the identification of static and variable strings and stored in the skeleton database. Document fields are validated after all document fields have been extracted.

Abstract: Systems and methods of reconciling data from an imaged document. In one embodiment, a business document is scanned to create a business document image. A set of extracted data is extracted from the business document image via optical character recognition (OCR). The set of OCR extracted data is then compared with data in business information management or enterprise resource planning (ERP) system. A set of ERP data is retrieved from the ERP system that relates to the set of OCR extracted data. The retrieved ERP data is than assigned to the set of OCR extracted data to create a set of assigned data. The business document image is then displayed in a business document image pane, the set of OCR extracted data is displayed in the OCR data pane, and the retrieved ERP data is displayed in the ERP data pane. The set of assigned data is validated, and the ERP system is updated with the set of validated, assigned data. In other embodiments, data is extracted from text files without using OCR.

Abstract: Embodiments of the invention relate to a software-as-a-service (“SaaS”) digital document management system (“DDMS”). The DDMS is located remotely from an office building or other place of business on a remote server. The server is connected to one or more databases for storing digital documents or information extracted from the digital documents. A user populates the DDMS by sending digital documents to the server using a computer, an email-enabled scanner, or the like. When a digital document is received at the server, the document is routed to a customer pending queue, information from the digital document is extracted, the digital document is stored in a directory, and the extracted information is stored in a relational database. The extracted information is supplemented or corrected as necessary using a web portal which allows a user to access the digital document and the extracted information.

Abstract: Systems and methods of recognizing a business document and creating a document signature. In one embodiment, a business document is scanned and a business document image is created. The business document image is compared to a template database. If a matching template is found, document fields are defined and extracted. If no matching document template is found, the document image is compared to a skeleton database. If a matching document skeleton in found, document fields are defined and extracted. A document skeleton is generated and then stored in the template database. If no matching document skeletons are found in the skeleton database, document fields are manually extracted. A document skeleton is then generated from the identification of static and variable strings and stored in the skeleton database. Document fields are validated after all document fields have been extracted.

Abstract: Systems and methods of reconciling data from an imaged document. In one embodiment, a business document is scanned to create a business document image. A set of extracted data is extracted from the business document image via optical character recognition (OCR). The set of OCR extracted data is then compared with data in business information management or enterprise resource planning (ERP) system. A set of ERP data is retrieved from the ERP system that relates to the set of OCR extracted data. The retrieved ERP data is than assigned to the set of OCR extracted data to create a set of assigned data. The business document image is then displayed in a business document image pane, the set of OCR extracted data is displayed in the OCR data pane, and the retrieved ERP data is displayed in the ERP data pane. The set of assigned data is validated, and the ERP system is updated with the set of validated, assigned data. In other embodiments, data is extracted from text files without using OCR.

Abstract: A general document recognition system is described which is intended to be used in connection with an electronic document transmission function used on a computer network. The general document recognition system is set up to recognize any number of document types created by application programs in the network and is also set up with rules as to how to extract destination data from each document type. The extracted data from each document can be the actual intended destination, such as a facsimile telephone number, or can be the identity of the intended recipient individual. If a recipient, rather than a destination, is extracted from the document, the general document recognition system can query a previously designated external database to recover the destination information for that recipient. An LDAP database is the preferred external database for this function.