In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner or multifunction peripheral.

Systems and methods for mobile image capture and processing

A method according to one embodiment includes extracting an identifier from an electronic first document, and identifying a complementary document associated with the first document using the identifier. A validity of the first document is determined by simultaneously considering: textual information from the first document; textual information from the complementary document; and predefined business rules. An indication of the determined validity is output. Systems and computer program products for providing, performing, and/or enabling the methodology presented above are also presented.

Systems, methods, and computer program products for determining document validity

In one embodiment, a method includes receiving a digital image captured by a mobile device; and using a processor of the mobile device: generating a first representation of the digital image, the first representation being characterized by a reduced resolution; generating a first feature vector based on the first representation; comparing the first feature vector to a plurality of reference feature matrices; and classifying an object depicted in the digital image as a member of a particular object class based at least in part on the comparing.

Systems and methods for classifying objects in digital images captured using mobile devices

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.

Systems and methods for identification document processing and business workflow integration

Techniques for an image-based CAPTCHA for object recognition are described. The disclosure describes adding images to a database by collecting images by querying descriptive keywords to an image search engine or crawling images from the Internet. The disclosure describes generating the image-based CAPTCHA. The image is retrieved from the database, along with objects having significant values. An object is cropped from its image. The portion on the image where the object has been cropped is filled with image inpainting. The process obtains other objects from the database. The object is mixed among the other objects to from a set of candidate objects. A user is asked to select “the object” from the set of candidate objects that fits or matches the image. The image-based CAPTCHA evaluates whether a response, the selection, is from a human or a bot.

Image-Based CAPTCHA Exploiting Context in Object Recognition

In a combined holistic and analytic recognition system, the holistic recognition module will recognize an input word or phrase image by matching an input string of character features for the whole word or phrase against a string of prototype features for a plurality of reference words in a lexicon. This will yield a holistic answer list of recognized word or phrase candidates for the input word or phrase along with a confidence value for each answer on the list. At the same time based on each answer in the answer list, the holistic recognition modules will generate a list of character features and segment the character features into sets of each character in an answer. The analytical recognition module uses segmentation hypotheses from the segmented character feature sets to cut the image of the input string of characters into individual character images. A plurality of character images for the various segmentation hypotheses will be recognized to produce an analytical answer list having a plurality of word or phrase answers for the input word or phrase. Each analytic word answer will have a confidence value based on the combined confidence of recognizing each character. The holistic answer list and the analytic answer list will be examined to find the best answer from the two lists as the recognition of the input handwritten text.

Arseni Longmont Seregin

Papers

Holistic-analytical recognition of handwritten text