Parse weekly planner PDFs

Parsing weekly planner PDFs usually involves extracting structured information like dates, tasks, events, and notes from the PDF file. Depending on how the PDF is formatted (text-based or scanned images), different techniques are used.

Here’s a detailed guide on how you can parse weekly planner PDFs programmatically:

1. Determine PDF Type

• Text-based PDF: Text content can be extracted directly.

• Scanned Image PDF: Requires OCR (Optical Character Recognition) to extract text.

2. Tools and Libraries

• For text-based PDFs:

  • Python libraries like PyMuPDF (fitz), pdfplumber, PyPDF2, or pdfminer.six

• For scanned/image-based PDFs:

  • OCR tools like Tesseract OCR with Python wrapper pytesseract

• Other helpful tools:

  • camelot or tabula-py for table extraction if the planner is tabular

  • pdf2image to convert PDF pages into images for OCR

3. Steps for Parsing Text-based Weekly Planner PDFs

a. Extract Text per Page

python
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import fitz  # PyMuPDF

doc = fitz.open("weekly_planner.pdf")
for page_num in range(len(doc)):
    page = doc.load_page(page_num)
    text = page.get_text("text")  # extract all text
    print(text)

b. Identify Weekly Sections

• Use regex or keyword-based parsing to split the text into days of the week or task sections.

• Example: If the planner lists days as Monday, Tuesday, etc., split the text accordingly.

python
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import re

# Suppose text contains the whole page's text
days = re.split(r'b(Monday|Tuesday|Wednesday|Thursday|Friday|Saturday|Sunday)b', text)
# This will split the text and keep day names as delimiters

c. Extract Tasks and Times

• Parse out timestamps or bullet points under each day.

• Structure the data into a dictionary or JSON.

4. Steps for Parsing Scanned Weekly Planner PDFs (OCR)

a. Convert PDF to Images

python
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from pdf2image import convert_from_path

images = convert_from_path('weekly_planner.pdf')

b. Run OCR on Each Image

python
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import pytesseract

for i, img in enumerate(images):
    text = pytesseract.image_to_string(img)
    print(f"Page {i+1} Text:n{text}")

c. Post-process Text as in Step 3

5. Parsing Tables (If the planner is in table format)

• Use camelot or tabula-py for tabular planners.

python
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import camelot

tables = camelot.read_pdf('weekly_planner.pdf', pages='1-end')
for table in tables:
    df = table.df  # pandas dataframe of the table
    print(df)

6. Organizing Parsed Data

• After extracting, organize the data into JSON like:

json
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{
  "Monday": [
    {"time": "9:00 AM", "task": "Team meeting"},
    {"time": "11:00 AM", "task": "Project review"}
  ],
  "Tuesday": [
    ...
  ]
}

7. Example Full Python Script (Basic Text Extraction + Parsing Days)

python
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import fitz
import re

def parse_weekly_planner(pdf_path):
    doc = fitz.open(pdf_path)
    weekly_data = {}

    for page_num in range(len(doc)):
        page = doc.load_page(page_num)
        text = page.get_text("text")

        # Split text by days
        chunks = re.split(r'b(Monday|Tuesday|Wednesday|Thursday|Friday|Saturday|Sunday)b', text)
        
        # chunks has day names and content alternating: ['', 'Monday', 'content', 'Tuesday', 'content', ...]
        for i in range(1, len(chunks), 2):
            day = chunks[i]
            content = chunks[i+1].strip()
            tasks = [line.strip() for line in content.split('n') if line.strip()]
            if day not in weekly_data:
                weekly_data[day] = []
            weekly_data[day].extend(tasks)

    return weekly_data

planner_data = parse_weekly_planner("weekly_planner.pdf")
print(planner_data)

If you want, I can help build a customized parser script tailored to your specific planner PDF format — just share a sample or details on the structure!