Extracting State Agency Call Center Wait Times from FOIA PDF
This PDF contains data on wait times at a state agency call center. The main focus is on the data on the first two pages, which matches other states' submission formats. The later pages provide granular breakdowns over several years. Challenges include it being heavily pixelated, making it hard to read numbers and text, with inconsistent and unreadable charts.
page.apply_ocr('surya')
page.find_all('text').show(crop=True)OCR and AI magic
Master OCR techniques with Natural PDF - from basic text recognition to advanced LLM-powered corrections. Learn to extract text from image-based PDFs, handle tables without proper boundaries, and leverage AI for accuracy improvements.
page.apply_ocr()page.apply_ocr('surya', resolution=192)page.apply_ocr(resolution=50)
page.find_all('text').inspect()Working with page structure
Extract text from complex multi-column layouts while maintaining proper reading order. Learn techniques for handling academic papers, newsletters, and documents with intricate column structures using Natural PDF's layout detection features.
page.find('table').apply_ocr()
text = page.extract_text()
print(text)cols = page.find_all('region[type=table-column]')
# Take one of the columns and apply OCR to it
cols[2].apply_ocr()
text = cols[2].extract_text()
print(text)table_area = page.find("region[type=table]")
table_area.apply_ocr()