System and method for generating best potential rectified data based on past recordings of data

A system for data processing, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• receive a digital document;
• apply an optical character recognition (OCR) algorithm on said received digital document by utilizing an OCR tool;
• identify defective data extracted by the OCR tool resulted from relatively inferior image quality of the received digital document;
• implement an auto rectification algorithm on the identified defective data;
• automatically generate, in response to implementing the auto rectification algorithm, corresponding auto-rectified data for each identified defective data;
• record the defective data and corresponding auto-rectified data at a field level;
• receive user input data on said recorded auto-rectified data;
• determine whether the auto-rectified data is correct or not;
• populate, based on determining that the auto-rectified data is correct, a machine learning model with said received user input data to be utilized for subsequently received digital document;
• generate a plurality of first selectable icons, wherein each of said first selectable icon is configured to display corresponding auto-rectified field data when user input is received by clicking or hovering over the first selectable icon;
• receive user input data that the auto-rectified field data is not correct based on user's comparing comparison of the auto-rectified field data with a corresponding original image data of the digital document;
• and
• receive user input data indicating a user defined correct field data replacing the auto-rectified field data.

A non-transitory computer readable medium configured to store instructions for data processing, wherein, when executed, the instructions cause a processor to perform the following:

• receiving a digital document;
• applying an optical character recognition (OCR) algorithm on said received digital document by utilizing an OCR tool;
• identifying defective data extracted by the OCR tool resulted from relatively inferior image quality of the received digital document;
• implementing an auto rectification algorithm on the identified defective data;
• automatically generating, in response to implementing the auto rectification algorithm, corresponding auto-rectified data for each identified defective data;
• recording the defective data and corresponding auto-rectified data at a field level;
• receiving user input data on said recorded auto-rectified data;
• determining whether the auto-rectified data is correct or not;
• populating, based on determining that the auto-rectified data is correct, a machine learning model with said received user input data to be utilized for subsequently received digital document;
• generating a plurality of first selectable icons, wherein each of said first selectable icon is configured to display corresponding auto-rectified field data when user input is received by clicking or hovering over the first selectable icon;
• receiving user input data that the auto-rectified field data is not correct based on user's comparing comparison of the auto-rectified field data with a corresponding original image data of the digital document;
• and
• receiving user input data indicating a user defined correct field data replacing the auto rectified field data.