Check whole pdf and give important questions for MUHS exam . And give the how many types that question are repeated in which year and also give the important topic for the exam for study. Make the list of some questions they have High chances to come in exam 2026 of bsc nursing 2nd semester
cd /home/daytona/workspace/attachments/96975636-075c-4bda-b3d1-139d4d7dd6f1/ && …
which tesseract && tesseract --version 2>&1 | head -3
cd /home/daytona/workspace && mkdir -p nutrition_ocr && python3 -c " import subp…
cd /home/daytona/workspace/nutrition_ocr && pdftoppm -r 150 -png -f 1 -l 5 '/hom…
cd /home/daytona/workspace/nutrition_ocr && for i in $(seq -w 1 23); do f="pag…
cd /home/daytona/workspace/nutrition_ocr && python3 -c " import subprocess, os, …
cd /home/daytona/workspace/nutrition_ocr && python3 -c " import subprocess, glob…
You need to perform OCR on a set of scanned question paper images located at /home/daytona/workspace/nutrition_ocr/. The files are named page-01.png through page-23.png. These are MUHS (Maharashtra University of Health Sciences) BSc Nursing 2nd semester exam papers for "Nutrition and Biochemistry" (subject code 62502). Your job: 1. Use Python with pytesseract/tesseract to perform enhanced OCR on each page image. Try these preprocessing steps for better results: - Convert to grayscale - Increase contrast - Apply thresholding - Use --psm 4 or --psm 3 mode 2. For each page, extract ALL readable text. 3. Save combined output to /home/daytona/workspace/nutrition_ocr/enhanced_ocr.txt Here's the Python approach to try: ```python import subprocess, glob, os from PIL import Image, ImageEnhance, ImageFilter import pytesseract files = sorted(glob.glob('/home/daytona/workspace/nutrition_ocr/page-*.png')) all_text = [] for f in files: pg = os.path.basename(f) img = Image.open(f).convert('L') # Enhance contrast enhancer = ImageEnhance.Contrast(img) img = enhancer.enhance(2.0) # Threshold img = img.point(lambda x: 0 if x < 128 else 255) # OCR text = pytesseract.image_to_string(img, config='--psm 6 -l eng') all_text.append(f'=== {pg} ===\n{text}\n') with open('/home/daytona/workspace/nutrition_ocr/enhanced_ocr.txt', 'w') as out: out.write('\n'.join(all_text)) ``` 4. After saving, read the enhanced_ocr.txt file and extract: - All exam years visible in the papers - All questions from each year's paper - Question numbers and their text - Section labels (Section A, Section B, etc.) - Marks allocation if visible 5. Return a COMPLETE structured report with: - Year-wise list of all questions found - All text extracted from each page, even if partial - Note which pages had readable vs unreadable content Be thorough - this is critical exam preparation material. Return everything you find in your response.
~/nutrition_ocr
cd /home/daytona/workspace/nutrition_ocr && python3 -c " import subprocess, glob…
python3 -c " import subprocess, glob, os from PIL import Image, ImageEnhance, Im…
python3 -c " import subprocess, glob, os from PIL import Image, ImageEnhance, Im…