We have arrived at a solution with the project basis and our own research regarding the challenges in mind. They are listed below:

• The images are collected and organized as a coherent dataset for further implementations. The content is preprocessed and refined to make the data extraction as easy as possible.

• A Convolution Neural Network encoder module is used as the framework for encoding an image to it’s features - i.e., identifying the crucial elements of an image. These elements are the bedrock of our model. Our CNN module contains both pre-configured encoders but they can also extract information and develop encoders in real time.

• A word embedding model is based on a natural language processing technique, an operation to group together similar words and terms. These groupings are extracted when the decoded information from the image, they provide the details that are observed by the CNN in more simpler human parsable terms. This module is coupled also with an operation that could provide contextuality to the text, maintaining an structured result rather than a mere grouping of words. This provides a series of relational and dependent captions which are automatically scored by preference and totality.

• At last, a CNN decoder model - the inverse of the first encoder model - where the image vectors (and subsequently the images) are matched together with the highest probable and accurate caption predetermined by scoring.

• For deep interpretation of illustrations and other objects in the document a custom OCR is used for detection over a pre-defined, out-of-box, OCR method.