3/28/2023 0 Comments Pdf extract text boxes![]() ![]() First, I am going to define the structure by assigning some meaningful names to the columns. ![]() The easiest thing you can do is to check the length of each row and add the data to a dataframe if it matches the desired length. The visualization of the document shows that the lines which contain the numbers in our sheet are all 3 or 4 columns long. Possible storing structures #1 Save the information in a pandas DataFrame object. If you have any suggestions or remarks please send me an email.įor those who are interested: In the following section I will give some examples and show a few ways of how the extracted data could be stored. I hope you find this guide for extracting semi-structured text-data from pdf documents helpful. This is absolutely domain-dependent, so no generic solution exists. All you need to do now is to access the information you require. If you need those too, just add an if-statement to the arrange_and_extract_text() function. Note that rows containing only one character are empty. Voila! Here you can see a neat row-column-structure. Now we arrange them row-wise and finally look at what we are doing this for: the text. Structuring the text data row-column-wise By looking at the visualized document structure, I decided to approach this problem by also structuring the text row- and column-wise. Depending on the goal, there are several ways, each of them with its own advantages and disadvantages. We have to find another approach in order to get this data into structure. The lines may indicate headlines but this conclusion does not seem to be consistent throughout the document. Here you can see why I talk about semi-structured data: the content of the pdf is arranged in rows and columns but there are no real separators to easily distinguish between the end of one logical entity and the beginning of another. On the right, I plotted the bounding boxes of the characters and the TextBoxes. On the left, I plotted all lines/rectangles and the bounding boxes of the characters. subplots( 1, num_pages, figsize = ( num_pages * size, size * ( ymax / xmax)), sharey = True, sharex = True) bbox size = 6 num_pages = 2 fig, axes = plt. The contents of the document are anonymized. The following code - mainly taken from the blog-post mentioned above - will extract all LTPage objects from an example document. Each page in a PDF is described by a LTPage object and the hierarchical structure of lines, boxes, rectangles etc. The PDF layout we are dealing with comes in the form of a LTPage object. For the scale of a few thousand documents with multiple pages, a combination of the two was the best choice. PDFQuery turned out to be a lot faster (~5 times) in reading the document, while pdfminer provides the necessary tools to extract the layouts. Technical Detailsįor reading PDF files, I am using PDFQuery, while the extraction of the layout is done with the help of pdfminer. Using insights found on a blog post, the following pages will present what the contained data looks like and consider a more general solution for extracting data from PDFs. Semi-structured hereby refers to the fact that PDFs, in contrast to html, regularly contain information in varying structure: Headlines may or may not exist the number of pages often varies along with the size and position of characters. While many tools exist for data extraction, not all are suitable in every case. Towards a more general approach for extracting semi-structured dataįinancial data is often contained in semi-structured PDFs. Extracting Semi-Structured Data from PDFs on a large scale ![]()
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