# Training the document page layout and classifying good/bad layouts

I have a use case where I am supposed to get the coordinates of each block element in a page (whether its paragraph, image, table) where I train a model to understand how they are placed in a given page where some documents are with good layout and other with bad ones and I want to train this and throw in some coordinates of a new document and try to understand whether it has a good layout or a bad layout, I want to understand how I can achieve this using some deep learning techniques ?

can someone suggest me an approach for solving this?

Was trying to workout with RNN but not sure if that's the correct approach.

• This depends very much on what kind of data you have, are all the documents of the same type (e.g all CV's). Furthermore is there any specific reason you want an RNN, would a random forest not do just as well (you'd need to do some feature engineering to extract good data for it though). Aug 21, 2019 at 12:29
• Yes, all the documents are of the same type, Not exactly which one would be doing good so was asking for a recommendation. What kinds of features would be helpful for building the system? Aug 21, 2019 at 12:31
• This would depend on how you want to decide whats a good layout or not. I would imagine maximum, minimum and average font height, the type of the first through too however many is most effective objects on the page, names of headings, page margin sizes etc Aug 23, 2019 at 13:02
• Got it but what approach would be better for a model generation? like which one is preferred? Aug 26, 2019 at 9:05
• I'd probably do both, train an series of ML models and if they perform well enough stop. If you need more accuracy try a Multi layer perceptron then full AI Aug 27, 2019 at 10:25

number header1 header2 header3 paragraph1 font-size-h1 font-size-h2 target