Systems and methods for automated document image orientation correction

An apparatus for correcting an orientation of an image data object, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least:

• receive an original image data object;
• generate, at least in part by applying an optical character recognition (OCR) process, initial machine readable text for the original image data object;
• generate, at least in part by using one or more machine learning models, an initial quality score for the initial machine readable text;
• determine whether the initial quality score satisfies one or more quality criteria;
• responsive to determining that the initial quality score does not satisfy the one or more quality criteria, generate a plurality of rotated image data objects each corresponding to a different rotational position, wherein each of the plurality of rotated image data objects comprise the original image data object rotated to a corresponding rotational position;
• generate a rotated machine readable text data object for each of the plurality of rotated image data objects, wherein each of the rotated machine readable text data objects are stored in association with corresponding rotated image data objects;
• generate, at least in part by using one or more machine learning models, a rotated quality score for each of the rotated machine readable text data objects;
• determine that a first rotated quality score of the rotated quality scores satisfies the one or more quality criteria, wherein the first rotated quality score corresponds to a first rotated machine readable text data object of the rotated machine readable text data objects;
• and
• provide the first rotated machine readable text data object to a natural language processing (NLP) engine.

A computer program product for correcting an orientation of an image data object, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:

• receive an original image data object;
• generate, at least in part by applying an optical character recognition (OCR) process, initial machine readable text for the original image data object;
• generate, at least in part using one or more machine learning models, an initial quality score for the initial machine readable text;
• determine whether the initial quality score satisfies one or more quality criteria;
• responsive to determining that the initial quality score does not satisfy the one or more quality criteria, generate a plurality of rotated image data objects each corresponding to a different rotational position, wherein each of the plurality of rotated image data objects comprise the original image data object rotated to a corresponding rotational position;
• generate a rotated machine readable text data object for each of the plurality of rotated image data objects, wherein each of the rotated machine readable text data objects are stored in association with corresponding rotated image data objects;
• generate, at least in part by using one or more machine learning models, a rotated quality score for each of the rotated machine readable text data objects;
• determine that a first rotated quality score of the rotated quality scores satisfies the one or more quality criteria, wherein the first rotated quality score corresponds to a first rotated machine readable text data object of the rotated machine readable text data objects;
• and
• provide the first rotated machine readable text data object to a natural language processing (NLP) engine.