Abstract: A data extraction system for receiving and scanning documents to generate ordered input for storage in a database employs a non-linear statistical model for a data extraction sequence having a plurality of transformations. Each transformation transitions an extracted data value in various forms from a raw data image to a computed data value. For each transformation, a confidence model learns a confidence component for the particular transformation. The learned confidence components, generated from a control set of documents having known values, are employed in a production mode with actual raw data. The confidence component corresponds to a likelihood of transformation accuracy, and the confidence model aggregates the confidence components to compute a confidence for the extracted data value. A database stores the extracted data value labeled with the computed confidence attribute for subsequent use by an application employing the extracted data.

Abstract: Techniques disclosed herein include systems and methods for data validation and correction. Such systems and methods can reduce costs, improve productivity, improve scalability, improve data quality, improve accuracy, and enhance data security. A data manager can execute such data validation and correction. The data manager identifies one or more anomalies from a given data set using both contextual information and validation rules, and then automatically corrects any identified anomalies or missing information. Identification of anomalies includes generating similar data elements, and correlating against contextual information and validation rules.

Abstract: Techniques disclosed herein include systems and methods for data validation and correction. Such systems and methods can reduce costs, improve productivity, improve scalability, improve data quality, improve accuracy, and enhance data security. A data manager can execute such data validation and correction. The data manager identifies one or more anomalies from a given data set using both contextual information and validation rules, and then automatically corrects any identified anomalies or missing information. Identification of anomalies includes generating similar data elements, and correlating against contextual information and validation rules.