PDFs are a fantastic way to store information since they combine text, images, and attractive visual design, but many times we only need the text. Converting a rich PDF to plain text is useful for search engine indexing, for searching keywords within documents, and for passing content to artificial intelligence services for analysis and knowledge generation.
Before starting, get free credentials on the Foxit developer portal. These credentials include a client ID and a client secret that you will need to call the Foxit REST APIs.
Overview of the flow with Python: the typical flow consists of uploading the document, starting the extraction task, checking the task status, and downloading the result. In practical terms, four main functions are usually implemented: one to upload the PDF and obtain a documentId, one that starts the extractPDF extraction indicating the extraction type, one that checks the task status checkTask, and another that downloads the result getResult and returns the content as text.
The extraction API accepts three key parameters: the ID of the previously uploaded document, the extraction type which can be TEXT to get text, IMAGE to receive a zip with the PDF images, or PAGE to extract specific pages into a new PDF, and optionally a page range that can combine specific pages and continuous ranges. If no range is specified, the extraction is performed on the entire document.
In practice with Python, the flow is summarized in these conceptual steps: upload the PDF with uploadDoc to obtain documentId, request the extraction with extractPDF passing the TEXT type, wait for the task to finish with checkTask, and finally retrieve the textual result with getResult and save it in a text variable for further processing.
Once the text is available, the possibilities are many. For example, we can automate keyword searching across a set of PDFs. Instead of manually processing each file, the script goes through an input folder, uploads each PDF, requests text extraction, gets the text, and checks if a keyword appears in the content. If the term appears, an action can be triggered such as routing the document, generating an email alert, or storing metadata.
Example of practical use: process all PDF files in a folder, extract the text, and search for the word Shakespeare. If the term is found, the finding is recorded and the file can be moved to a reviewed folder or the person responsible for the document flow can be notified.
To optimize costs and times, it is recommended to implement a result cache. Saving the text extraction associated with the PDF name using the .txt extension allows you to avoid calling the API again if the file has not changed. This is useful when keywords or business rules change over time, since only the already extracted text can be re-analyzed without reprocessing the PDF.
This type of automation can easily be extended to include automatic routing of documents with matches, email alerts, integration with content managers, or triggers in cloud services. The Foxit API also allows extracting images and pages, which expands the options for visual validation processes or for creating mini documents with only the relevant pages.
If you want to go further, check the official documentation of the Foxit APIs and register on the developer portal to get your free credentials. In the developer forums you can find examples and community support to integrate these capabilities into more complex document pipelines.
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