Pypdf2 extract text no spaces
- Pypdf2 extract text no spaces how to#
- Pypdf2 extract text no spaces software#
- Pypdf2 extract text no spaces series#
Pypdf2 extract text no spaces how to#
For reasons I don’t entirely recall, but related to struggling to conceptualize how to write a function that would separate the PDFs and extract the text, I chose Automator, a default Mac utility, to separate the pages and extract the text from the PDF files. It is worth noting that not all of the methods I used to prepare my corpus are ones that I would recommend. Having determined the unit of analysis, the technical challenge was how to split the PDF documents and extract the text. 1 Using the page as the unit of analysis enables me to interact with the articles as well as the community that the collection of articles creates. This juxtaposition of items creates what Marshall McLuhan refers to as the “human interest” element of news print, constructed through the “mosaic” of the page layout. Rather than interacting with each article in isolation (as is modeled in many database editions of historical newspapers), the newspaper readers would experience an article within the context of the other stories on the page. In addition, using the page as the unit of analysis is more reflective of the print reading experience. Splitting on the page is also computationally and analytically simple, which is valuable when working at the scale of this project. While not all pages are created equal (early issues of the Review and Herald made great use of space and small font size to squeeze about 3000 words on a page), on average the pages contain about 1000 words, placing them in line with the units Matthew Jockers has found to be most useful when modeling novels. In the end, I chose the middle-ground solution of using the page as the document unit. In addition, the publications contain a number of article “edge cases”, such as advertisements, notices of letters received, and subscription information, which would either need to be identified and separated into their own articles or identified and excluded. But the boundaries of “articles” in a newspaper type publication are actually rather hard to define, and the length of the candidate sections range from multiple-page essays to one paragraph letters or poems. For this, the “article” seemed like a very useful unit, enabling each distinct piece to be examined on its own. Since I am interested in identifying shifts in discourse over time, a more fine-grained unit was necessary. With issues ranging in length from 8 pages to 100 pages, and including a variety of elements from long essays to letters to the editor and field reports, I would only be able to surface summary patterns using the issue as a whole. I quickly dismissed using the “issue” because it is too large and too irregular a unit. In extracting the text, I also had to determined my unit of analysis for text mining – the article, the page, or the issue.
![pypdf2 extract text no spaces pypdf2 extract text no spaces](https://i.stack.imgur.com/J8Mgy.png)
That lack of information sets up the challenge for the next section of this module, which documents my work to assess and clean the corpus, previewed in an earlier blog post. But unlike the newspapers scanned as part of the Chronicling America project, there is very little information embedded in these files about the source and estimated quality of that OCR. The PDF files produced by the Office of Archives and Statistics include the produced OCR.
![pypdf2 extract text no spaces pypdf2 extract text no spaces](https://cdn.analyticsvidhya.com/wp-content/uploads/2020/08/Untitled-design9.png)
One advantage of this is that you then have control over the OCR software, but it significantly increases the time and complexity of the text gathering process.
Pypdf2 extract text no spaces software#
But for the researcher, this necessitates adding a text recognition step to the gathering process, running the pages through OCR software to generate the needed text layer. As many people want textual data, and preferably good textual data, for a variety of potentially lucrative computational tasks, it makes sense for companies to withhold the text layer. It is not uncommon when downloading books scanned to PDFs from providers such as Google to discover that they have only made the page images available. Here my choice of source base offered some advantages and some additional challenges. With the PDF files downloaded, my next challenge was to extract the text. Second, I am sharing them in hopes that “ given enough eyeballs, all bugs are shallow.” First, I hope that they might prove useful to others interested in taking on similar projects.
![pypdf2 extract text no spaces pypdf2 extract text no spaces](https://automatetheboringstuff.com/2e/images/000103.jpg)
![pypdf2 extract text no spaces pypdf2 extract text no spaces](https://s3.amazonaws.com/stackabuse/media/working-with-pdfs-python-reading-splitting-1.png)
My goals in sharing the notebooks and technical essays are two-fold. You can access the Jupyter notebooks on Github. For an overview of the dissertation project, you can read the current project description at.
Pypdf2 extract text no spaces series#
This is part of a series of first drafts of the technical essays documenting the technical work that undergirds my dissertation, A Gospel of Health and Salvation.