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

A method, comprising:

• receiving or capturing a digital image using a mobile device;
• using a processor of the mobile device:
• determining whether an object depicted in the digital image belongs to a particular object class among a plurality of object classes;
• displaying the digital image on a display of the mobile device upon determining the object does not belong to any of the plurality of object classes;
• receiving user input via the display of the mobile device, the user input identifying one or more regions of interest in the object;
• building a feature vector based at least in part on the user input;
• building and/or selecting an extraction model based at least in part on the feature vector;
• extracting data from the digital image based at least in part on the extraction model;
• and
• detecting one or more lines of text in the digital image, the detecting comprising
• detecting a plurality of connected components non-background elements in the digital image, and
• determining a plurality of likely characters based on the plurality of connected components, wherein determining the plurality of likely characters comprises determining whether each of the plurality of connected components is characterized by a predetermined number of light-to-dark transitions in a predetermined direction.

A computer program product comprising:

• non-transitory computer readable storage medium having program code embodied therewith, the program code readable/executable by a mobile device comprising a processor to:
• receive or capture a digital image using the mobile device;
• use the processor to:
• determine whether an object depicted in the digital image belongs to a particular object class among a plurality of object classes based on feature-space discrimination, wherein the feature space discrimination utilizes one or more of support-vector-machine (SVM) techniques, transductive classification techniques, and maximum entropy discrimination (MED) techniques;
• determine one or more object features of the object based at least in part on the particular object class and at least partially in response to determining the object belongs to the particular object class;
• build or select an extraction model based at least in part on the one or more determined object features;
• and
• extract data from the digital image using the extraction model, the extracting comprising detecting one or more lines of text in the object, and the detecting comprising:
• projecting the digital image onto a single dimension;
• projecting each color channel of the digital image onto a single channel along the single dimension
• determining a distribution of light and dark areas along the projection;
• determining a plurality of dark pixel densities, each dark pixel density corresponding to a position along the projection;
• determining whether each dark pixel density is greater than a probable text line threshold;
• and
• designating each position as a text line upon determining the corresponding dark pixel density is greater than the probable text line threshold.