# Best way to build gensim WdmSimilarity for document data

I'm building an application that searches for queries in OCR data. My documents are numerous and have many pages.

I'm using Wmd Similarity to query my data with gensim

instance = WmdSimilarity(text_data_preproc, model, num_best=10)


Given that I'm working with a large dataset of OCR data from documents, what should be fed into this function to get better results among the following options?

• The text each page only (one page at time)
• The text of the full document
• The text of all documents togheter

I'm using GloVe as my model.