Extracting table data from documents with OCR usually means building a pipeline with these steps:

1) Detect the document type

• Scanned PDF/image: needs OCR.
• Digital PDF: may already contain text, so you can extract directly without OCR.

2) Preprocess the image

Improve OCR accuracy before extraction:

• deskew/rotate
• denoise
• increase contrast
• binarize
• crop margins
• upscale low-resolution scans

3) Run OCR

Use an OCR engine to get:

• text
• bounding boxes
• confidence scores

Common tools:

• Tesseract (open source)
• PaddleOCR
• Google Document AI
• AWS Textract
• Azure Document Intelligence
• ABBYY FineReader

4) Detect the table structure

This is the key step. You need to identify:

• table boundaries
• rows
• columns
• cell borders or alignment
• merged cells

Approaches:

• Rule-based: works if tables have clear lines or consistent spacing
• Deep learning / document AI models: better for complex layouts
• OCR + layout analysis: detect words and their positions, then reconstruct the table from geometry

5) Reconstruct the table

Map OCR text into cells using:

• x/y coordinates
• nearest-neighbor grouping
• line detection for ruled tables
• column clustering for borderless tables

Then output to:

• CSV
• Excel
• JSON
• database tables

6) Validate and clean

Post-process to fix:

• broken rows
• split cells
• misread characters
• number/date formatting
• merged headers

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Practical options

If the table has visible lines

Use line detection + OCR:

• OpenCV to detect horizontal/vertical lines
• segment cells
• OCR each cell individually

This is often accurate for scanned forms and reports.

If the table is borderless

Use layout-based extraction:

• detect word positions
• cluster by rows and columns
• infer structure from alignment and whitespace

This is harder and may need ML-based tools.

If you want a managed service

Best options:

• AWS Textract: strong table extraction
• Google Document AI
• Azure Document Intelligence

These can return structured tables directly, which is much easier than doing it manually.

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Simple Python example with Tesseract + OpenCV

If the table has grid lines, a common approach is:

1. Convert image to grayscale
2. Threshold it
3. Detect horizontal and vertical lines
4. Find cell contours
5. OCR each cell

Pseudo-code:

import cv2
import pytesseract

img = cv2.imread("table.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(
    gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv2.THRESH_BINARY, 15, -2
)

# Detect lines and cells here with morphology
# Then crop each cell and run OCR:
text = pytesseract.image_to_string(cell_image)

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Recommended workflow

• For quick and accurate results: use AWS Textract / Google Document AI / Azure Document Intelligence
• For open-source customization: use OpenCV + Tesseract/PaddleOCR
• For complex documents: use layout models like LayoutLM, Detectron2-based table detectors, or vendor APIs

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If you want, I can also give you:

1. a Python script to extract tables from scanned PDFs,
2. a Tesseract/OpenCV approach, or
3. a comparison of OCR/table extraction tools.