Method and system of classifying documents based on layout determination

A system of classifying a document, the system comprising:

• a processor;
• and
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution by the processor, cause the processor to:
• determine line-text data for each of a plurality of lines of the document using a text extraction technique;
• determine a set of unique keywords in the document from a predefined list of keywords based on detection of at least one alias corresponding to each of the set of keywords in the line-text data for each of the plurality of lines,
• wherein the set of unique keywords are determined in a pre-defined reading sequence of the plurality of lines of the document;
• determine a feature matrix for the set of unique keywords by:
• for each keyword in the set of unique keywords:
• determine two forward nodes as next two subsequent keywords in the set of unique keywords based on determination of a shortest distance between position of the corresponding keyword and positions of the next two subsequent keywords and based on the pre-defined reading sequence;
• and
• determine weights of each of the two forward nodes based on the shortest distance and an angle of each of the two forward nodes with respect to the corresponding keyword;
• and
• determine a document layout of the document by:
• determine a cluster from a plurality of clusters based on the feature matrix using a machine learning clustering model,
• wherein the each of the plurality of clusters correspond to a unique document layout from a plurality of document layouts, and
• wherein the machine learning clustering model is trained based on training data comprising a plurality of documents corresponding to each of the plurality of document layouts.

A non-transitory computer-readable medium storing computer-executable instructions for classifying a document, the computer-executable instructions configured for:

• determining line-text data for each of a plurality of lines of the document using a text extraction technique;
• determining a set of unique keywords in the document from a predefined list of keywords based on detection of at least one alias corresponding to each of the set of keywords in the line-text data for each of the plurality of lines,
• wherein the set of unique keywords are determined in a pre-defined reading sequence of the plurality of lines of the document;
• determining a feature matrix for the set of unique keywords by:
• for each keyword in the set of unique keywords:
• determining two forward nodes as next two subsequent keywords in the set of unique keywords based on determination of a shortest distance between position of the corresponding keyword and positions of the next two subsequent keywords and based on the pre-defined reading sequence;
• determining weights of each of the two forward nodes based on the shortest distance and an angle of each of the two forward nodes with respect to the corresponding keyword;
• and
• determining a document layout of the document by:
• determining a cluster from a plurality of clusters based on the feature matrix using a machine learning clustering model,
• wherein the each of the plurality of clusters correspond to a unique document layout from a plurality of document layouts, and
• wherein the machine learning clustering model is trained based on training data comprising a plurality of documents corresponding to each of the plurality of document layouts.