Abstract:
In one embodiment, a method includes receiving an image of a tender document; performing optical character recognition (OCR) on the image; extracting an identifier of the tender document from the image based at least in part on the OCR; comparing the extracted identifier with content from one or more data sources; requesting complementary information from at least one of the one or more data sources based at least in part on the extracted identifier; receiving the complementary information; and outputting at least some of the complementary information for display on a mobile device. Exemplary systems and computer program products are also described.
Abstract:
A method according to one embodiment includes performing optical character recognition (OCR) on an image of a first document; generating a list of hypotheses mapping the first document to a complementary document using: textual information from the first document, textual information from the complementary document, and predefined business rules; at least one of: correcting OCR errors in the first document, and normalizing data from the complementary document, using at least one of the textual information from the complementary document and the predefined business rules; determining a validity of the first document based on the hypotheses; and outputting an indication of the determined validity. Additional systems, methods and computer program products are also presented.
Abstract:
According to one aspect, a computer-implemented method of discovering processes for robotic process automation (RPA) includes: recording a plurality of event streams, each event stream corresponding to a human user interacting with a computing device to perform one or more tasks; concatenating the event streams; segmenting some or all of the concatenated event streams to generate one or more individual traces performed by the user interacting with the computing device, each trace corresponding to a particular task; clustering the traces according to a task type; identifying, from among some or all of the clustered traces, one or more candidate processes for robotic automation; prioritizing the candidate processes; and selecting at least one of the prioritized candidate processes for robotic automation. Further aspects building upon the above include generating RPA models to perform tasks determined to be processes for RPA. Corresponding systems and computer program products are also described.
Abstract:
According to one aspect, a computer-implemented method of discovering processes for robotic process automation (RPA) includes: recording a plurality of event streams, each event stream corresponding to a human user interacting with a computing device to perform one or more tasks; concatenating the event streams; segmenting some or all of the concatenated event streams to generate one or more individual traces performed by the user interacting with the computing device, each trace corresponding to a particular task; clustering the traces according to a task type; identifying, from among some or all of the clustered traces, one or more candidate processes for robotic automation; prioritizing the candidate processes; and selecting at least one of the prioritized candidate processes for robotic automation. Further aspects building upon the above include generating RPA models to perform tasks determined to be processes for RPA. Corresponding systems and computer program products are also described.
Abstract:
A method includes: receiving or capturing an image comprising an identity document (ID) using a mobile device; classifying the ID; analyzing the ID based at least in part on the ID classification; determining at least some identifying information from the ID; at least one of building an ID profile and updating the ID profile, based at least in part on the analysis; providing at least one of the ID and the ID classification to a loan application workflow and/or a new financial account workflow; and driving at least a portion of the workflow based at least in part on the ID and the ID classification. Corresponding systems and computer program products are also disclosed.
Abstract:
Techniques for improved binarization and extraction of information from digital image data are disclosed in accordance with various embodiments. The inventive concepts include independently binarizing portions of the image data on the basis of individual features, e.g. per connected component, and using multiple different binarization thresholds to obtain the best possible binarization result for each portion of the image data independently binarized. Determining the quality of each binarization result may be based on attempted recognition and/or extraction of information therefrom. Independently binarized portions may be assembled into a contiguous result. In one embodiment, a method includes: identifying a region of interest within a digital image; generating a plurality of binarized images based on the region of interest using different binarization thresholds; and extracting data from some or all of the plurality of binarized images. Corresponding systems and computer program products are also disclosed.
Abstract:
Systems, computer program products, and techniques for detecting objects depicted in digital image data are disclosed, according to various exemplary embodiments. The inventive concepts uniquely utilize internal features to accomplish object detection, thereby avoiding reliance on detecting object edges and/or transitions between the object and other portions of the digital image data, e.g. background textures or other objects. The inventive concepts thus provide an improvement over conventional object detection since objects may be detected even when edges are obscured or not depicted in the digital image data. In one aspect, a computer-implemented method of detecting an object depicted in a digital image includes: detecting a plurality of identifying features of the object, wherein the plurality of identifying features are located internally with respect to the object; and projecting a location of one or more edges of the object based at least in part on the plurality of identifying features.
Abstract:
According to one embodiment, a computer-implemented method for confirming/rejecting a most relevant example includes: generating a binary decision model by training a binary classifier using a plurality of training documents; classifying one or more test documents into one of a plurality of categories using the binary decision model, wherein the one or more test documents lack a user-defined category label; selecting a most relevant example of the classified test documents from among the classified test documents; displaying, using a display of the computer, the most relevant example of the classified test documents to a user; receiving, via the computer and from the user, a confirmation or a negation of a classification label of the most relevant example of the classified test documents; and storing the confirmation or the negation of the classification label of the most relevant example of the classified test documents to a memory of the computer.
Abstract:
According to one embodiment, a computer-implemented method includes: capturing an image of a document using a camera of a mobile device; performing optical character recognition (OCR) on the image of the document; extracting an identifier of the document from the image based at least in part on the OCR; comparing the identifier with content from one or more reference data sources, wherein the content from the one or more reference data sources comprises global address information; and determining whether the identifier is valid based at least in part on the comparison. The method may optionally include normalizing the extracted identifier, retrieving additional geographic information, correcting OCR errors, etc. based on comparing extracted information with reference content. Corresponding systems and computer program products are also disclosed.
Abstract:
A method includes receiving or capturing an image comprising an identity document (ID) using a mobile device; classifying the ID; building an extraction model based on the ID classification; extracting data from the ID based on the extraction model; building an ID profile based on the extracted data; storing the ID profile to a memory of the mobile device; detecting a predetermined stimulus in a workflow; identifying workflow-relevant data in the stored ID profile at least partially in response to detecting the predetermined stimulus; providing the workflow-relevant data from the stored ID profile to the workflow; and driving at least a portion of the workflow using the workflow-relevant data. Related systems and computer program products are also disclosed.