5. In an automated data extraction environment, a non-transitory computer readable storage medium having instructions that when executed by a processor responsive to the instructions, perform a method of generating a confidence attribute comprising: scanning a set of data items, each data item corresponding to a control data value; generating, in a series of transformations, a recognized value from the scanned data item, the series of transformations defining a combined sequence from an input stream of data form to a final data value to convert the scanned data item to the recognized value, each of the transformations in the series converting the data item to a different form of the recognized value; determining, for each of the transformations, a component confidence, the component confidence indicative of a likelihood of accurate conversion from an input form to the different form; combining each of the component confidences determined for a particular data item in a weighted manner using a non-linear statistical model to generate a final confidence; and comparing, in a learning mode, the recognized value from a final transformation of the data item to the corresponding control data value, the recognized value defining a business relevant data value, the final confidence being indicative of the data value being a true representation of the original value in the input data stream.
5. In an automated data extraction environment, a non-transitory computer readable storage medium having instructions that when executed by a processor responsive to the instructions, perform a method of generating a confidence attribute comprising: scanning a set of data items, each data item corresponding to a control data value; generating, in a series of transformations, a recognized value from the scanned data item, the series of transformations defining a combined sequence from an input stream of data form to a final data value to convert the scanned data item to the recognized value, each of the transformations in the series converting the data item to a different form of the recognized value; determining, for each of the transformations, a component confidence, the component confidence indicative of a likelihood of accurate conversion from an input form to the different form; combining each of the component confidences determined for a particular data item in a weighted manner using a non-linear statistical model to generate a final confidence; and comparing, in a learning mode, the recognized value from a final transformation of the data item to the corresponding control data value, the recognized value defining a business relevant data value, the final confidence being indicative of the data value being a true representation of the original value in the input data stream. 19. The method of claim 5 wherein the set of data items is a training data set, the training data set further defining a control data set indicative of accurately captured data items, the control data set representing an accurate translation derived from the corresponding input documents, further including building the confidence model by comparing the scanned data item to a control set.