Method and system of comparing digital labels

A system for comparing at least two digital labels, the system comprising:

• a processor;
• and
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
• detect a set of regions of interest (ROIs) corresponding to a set of objects in each of the at least two digital labels using a first Machine Learning (ML)/Deep Learning (DL) model;
• classify each of the set of objects into one of a set of text objects or a set of non-text objects using a second ML/DL model;
• for each text object of the set of text objects,
• extract one or more key-value pairs of one or more attributes from the text object using a first Natural Language Processing (NLP) model;
• and
• compare the one or more key-value pairs and text data corresponding to the text object of the at least two digital labels using a second NLP model;
• for each non-text object of the set of non-text objects,
• categorize the non-text object into an object category from a set of object categories using a third ML/DL model;
• and
• compare an ROI of the non-text object based on positional information and the object category in the at least two digital labels;
• and
• generate an output report based on the comparison of the text data and the comparison of the ROI of each of the set of similar type of objects in the at least two digital labels.

A non-transitory computer-readable medium storing computer-executable instructions for comparing at least two digital labels, the computer-executable instructions configured for:

• detecting a set of regions of interest (ROIs) corresponding to a set of objects in each of the at least two digital labels using a first Machine Learning (ML)/Deep Learning (DL) model;
• classifying each of the set of objects into one of a set of text objects or a set of non-text objects using a second ML/DL model;
• for each text object of the set of text objects,
• extracting one or more key-value pairs of one or more attributes from the text object using a first Natural Language Processing (NLP) model;
• and
• comparing the one or more key-value pairs and text data corresponding to the text object of the at least two digital labels using a second NLP model;
• for each non-text object of the set of non-text objects,
• categorizing, by the processor, the non-text object into an object category from a set of object categories using a third ML/DL model;
• and
• comparing, by the processor, an ROI of the non-text object based on positional information and the object category in the at least two digital labels;
• and
• generating, by the processor, an output report based on the comparison of the text data and the comparison of the ROI of each of the set of similar type of objects in the at least two digital labels.