Method of classifying a document for a straight-through processing

A system of classifying a document for processing using a memory enabling model, the system comprising:

• a memory;
• and
• at least one processor communicably coupled to the memory, wherein the at least one processor is configured to:
• receive, from a storage device, an input image of the document;
• perform content extraction from the input image using a document extractor machine learning (ML) model, wherein the content extraction indicates extracting data from the input image into at least one of a constant field, a discrete field, and a variable field respectively;
• predict a template from a template dataset using a prediction ML model, based on the input image, wherein the predicted template has highest matching probability with the input image;
• determine a confidence score by comparing the input image and the predicted template, wherein the confidence score indicates similarity between the predicted template and the input image;
• postprocess the extracted content based on the predicted template upon determining that the confidence score is more than a pre-defined threshold confidence score;
• validate the extracted content based on the postprocessing of the extracted content, wherein the validation comprises of an image quality validation, a refined content extraction validation and a layout validation;
• classify the document for a straight through processing if the extracted content is successfully validated corresponding to each of the image quality validation, the refined content extraction validation, and the layout validation;
• classify the document for a manual annotation, different from the straight through processing if the extracted content is unsuccessfully validated corresponding to each of the image quality validation, the refined content extraction validation, and the layout validation;
• receive a correction input based on the manual annotation, wherein the correction input indicates manual content extraction from the input image;
• learn the correction input using a reinforcement learning model;
• and
• train the memory enabling model for performing the content extraction from another image similar to the input image using the manually annotated document.