Detect tables in scanned PDFs

Detecting tables in scanned PDFs can be a bit tricky because scanned documents are typically image-based, as opposed to text-based PDFs. To detect tables in scanned PDFs, you’ll need to employ Optical Character Recognition (OCR) along with table detection techniques. Here’s a step-by-step approach to do that:

1. OCR (Optical Character Recognition):

• First, you’ll need to extract text from the scanned PDF. Use an OCR tool to convert the image of the scanned page into machine-readable text.

• Popular OCR tools include:

  • Tesseract: An open-source OCR engine that works well for a variety of languages.

  • Adobe Acrobat Pro: Has a built-in OCR feature.

  • Google Cloud Vision API: A powerful cloud-based OCR solution.

  • Amazon Textract: Specialized in extracting text and structured data like tables from documents.

2. Table Detection:

After extracting text, you’ll need to identify and extract tables. Some methods for this include:

• Structured Text Extraction: Use OCR tools like Tesseract combined with regex or heuristics to detect patterns (such as rows and columns). This is effective if the table structure is simple and consistent.

• Machine Learning Models: If the document has more complex tables (e.g., nested tables, merged cells), machine learning-based methods might be necessary. Libraries like camelot-py or tabula-py can be useful for detecting tables in PDFs when they are not scanned images but are digitally created.

  • Camelot: An open-source Python library designed to extract tables from PDFs. It works best with digital PDFs, but it can sometimes handle scanned documents after OCR processing.

  • Tabula: Similar to Camelot, this library is designed for extracting tables from PDFs, although it’s typically more reliable with digitally created PDFs.

3. Post-OCR Table Structure Recognition:

Once the text is extracted, you may need to apply further processing to recognize the layout and structure of the tables, especially if the scanned document’s layout is not clean. Some tools use:

• OpenCV: For image preprocessing and detecting the visual structure of tables.

• PyPDF2 or pdfplumber: To help identify any patterns or structures in text that resemble tables (e.g., spaces, lines, or consistent text formatting).

4. Use PDF Parsing Libraries:

After OCR conversion, you can apply Python libraries like pdfplumber or PyMuPDF to further process and refine the table detection. These libraries support both image-based and text-based PDFs and can help identify table-like structures.

5. Manual Review:

Even with advanced tools, OCR and table extraction can sometimes produce imperfect results, especially with complex or poorly scanned documents. Therefore, manual review and correction of the detected tables might be necessary, especially for critical applications.

Example Workflow:

1. Use Tesseract OCR or Google Vision OCR to extract text from a scanned PDF.

2. Use Camelot or pdfplumber to try and detect table structures from the text output.

3. If the results are not perfect, use OpenCV to detect lines and boundaries of tables in the scanned image.

4. Post-process the detected tables using Python or another scripting language to clean up formatting and structure.

This approach will give you a good starting point for detecting and extracting tables from scanned PDFs. If you need to process many documents, automating the steps with scripts would save a lot of time!