Text structure extract from PDF brainstorming

1. Use existing tools like grobid.


Use machine learning to get scientific paper structure data.  It has demo page at here, http://cloud.science-miner.com/grobid/ . My tests show that it can extract title and some other data, but still content maybe mixed with footer and headers.  As this one is designed for academic docs, so it may have some issues to other types of PDF.

2. Borrow the ideas like grobid, to build a system to adapt to the PDF types that you use.
Unified format PDf may get better result, but not for general PDF.

3. Convert a PDF to doc file, and then use doc tools to extract content structures out?


Some brain storming ideas:

1). Use tool like pdfclown to extract position and style info of the PDF text data.

2). Then for same category PDF share patterned style and positions, we have chance to find the structures of file.

3). And based on the style of text, it is possible have a tree-like text structure, but this tree may not match with the real chapters tree. This method can help on section levels and titles

4). How to find the main content text?
By statistical info of the text font, the biggest portion of the occurrence normally the text content section. As the main content has different styles with other part, it is possible get good result here.

5). By check from bottom of each page to center’s first line, if PDF have many pages and a unified footer format, then it possible to find what style and font is for footer, and possible find out the footer pattern by statistical.

6). Use same trick of footer, it is possible find out the header if page have.

7). If we have position and style info of the PDF text data.
Then we maybe can do classification based on the position and styles training too to find the basic structure of file.


Some similar work and papers

a chinese patente for this:

a ch team works called Xed



HP paper

a text classification algorithm
that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories.