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from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image, ImageFilter
import torch
import re
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
def clean_ocr_text(text):
text = text.replace(",", ".").replace("s", "5").replace("o", "0").replace("O", "0")
return re.sub(r"[^\d.kg]", "", text.lower())
def extract_unit_from_text(raw_text):
raw = raw_text.lower()
if "kg" in raw:
return "kg"
elif "g" in raw:
return "g"
return "g" # default fallback
def restore_decimal(text):
if re.fullmatch(r"\d{5}", text):
return f"{text[:2]}.{text[2:]}"
elif re.fullmatch(r"\d{4}", text):
return f"{text[:2]}.{text[2:]}"
return text
def extract_weight(image):
try:
image = image.resize((image.width * 2, image.height * 2), Image.BICUBIC)
image = image.filter(ImageFilter.SHARPEN)
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
raw_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
cleaned = clean_ocr_text(raw_text)
match = re.search(r"(\d{1,3}\.\d{1,3})\s*(kg|g)?", cleaned)
if match:
return f"{match.group(1)} {match.group(2) or ''}", raw_text
fallback = re.search(r"\d{4,5}", cleaned)
if fallback:
fixed = restore_decimal(fallback.group())
return f"{fixed}", raw_text
return f"No valid weight found | OCR: {cleaned}", raw_text
except Exception as e:
return f"Error: {str(e)}", ""
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