Method and system for evaluating quality of a document

A system for determining quality of a document image, comprising:

• one or more processors;
• a memory communicatively coupled to the one or more processors, wherein the memory stores a plurality of processor-executable instructions, which, upon execution, cause the one or more processors to:
• segment the document image into a plurality of regions, wherein each of the plurality of regions comprises text data;
• classify each of the plurality of regions into one of a plurality of image quality classes,
• wherein each of the plurality of regions is classified based on a determination of a highest prediction value from one of a plurality of machine learning models, and
• wherein each of the plurality of machine learning models is trained corresponding to one of the plurality of image quality classes;
• wherein the training of each of the plurality of machine learning models corresponding to one of the plurality of image quality classes comprises determining a training dataset for each of the plurality of image quality classes;
• wherein the one or more processors are further configured to determine the training dataset for each of the plurality of image quality classes by:
• segmenting, by a computing device, a training image into a plurality of regions, wherein each of the plurality of regions comprises text data;
• for each of the plurality of regions, performing optical character recognition (OCR) using two or more OCR systems to determine corresponding two or more OCR text data;
• determining text matching scores based on a comparison among the two or more OCR text data using a plurality of string matching techniques;
• determining a plurality of threshold values for the plurality of image quality classes based on a statistical analysis of the text matching scores for the plurality of string matching techniques and for the plurality of regions;
• clustering the plurality of regions into one of the plurality of image quality classes based on the plurality of threshold values;
• compute a cumulative quality score for the document image based on a weighted average of a number of regions classified into each of the plurality of image quality classes;
• and
• determine the quality of the document image based on the cumulative quality score.

A non-transitory computer-readable medium storing computer-executable instructions for determining quality of a document image, the computer-executable instructions, executed by one or more processors, configured for:

• segmenting the document image into a plurality of regions, wherein each of the plurality of regions comprises text data;
• classifying each of the plurality of regions into one of a plurality of image quality classes,
• wherein each of the plurality of regions is classified based on a determination of a highest prediction value from one of a plurality of machine learning models, and
• wherein each of the plurality of machine learning models is trained corresponding to one of the plurality of image quality classes;
• wherein to train each of the plurality of machine learning models corresponding to one of the plurality of image quality classes, the computer-executable instructions are configured for determining a training dataset for each of the plurality of image quality classes;
• wherein to determine the training dataset for each of the plurality of image quality classes, the computer-executable instructions are configured for:
• segmenting a training image into a plurality of regions, wherein each of the plurality of regions comprises text data;
• for each of the plurality of regions,
• performing optical character recognition (OCR) using two or more OCR systems to determine corresponding two or more OCR text data;
• determining text matching scores based on a comparison among the two or more OCR text data using a plurality of string matching techniques;
• determining a plurality of threshold values for the plurality of image quality classes based on a statistical analysis of the text matching scores based on the plurality of string matching techniques and for the plurality of regions;
• clustering the plurality of regions into one of the plurality of image quality classes based on the plurality of threshold values;
• computing a cumulative quality score for the document image based on a weighted average of a number of regions classified into each of the plurality of image quality classes;
• and
• determining the quality of the document image based on the cumulative quality score.