كيفية بناء مسار التعرف الضوئي على الحروف (OCR) من البداية إلى النهاية باستخدام تقنية التعرف الضوئي على الحروف (OCR) غير المحدودة من Baidu للحصول على صور عالية الدقة وتحليل ملفات PDF متعددة الصفحات

import subprocess, sys
def pip_install(*pkgs):
   subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs])
print(">> Installing dependencies (1-2 min)...")
pip_install(
   "transformers==4.57.1",
   "Pillow",
   "matplotlib",
   "einops",
   "addict",
   "easydict",
   "pymupdf",
   "psutil",
   "accelerate",
)
print(">> Done.")
import os
import torch
from transformers import AutoModel, AutoTokenizer
assert torch.cuda.is_available(), (
   "No GPU detected! In Colab: Runtime -> Change runtime type -> GPU."
)
gpu_name = torch.cuda.get_device_name(0)
print(f">> GPU: {gpu_name}")
use_bf16 = torch.cuda.is_bf16_supported()
DTYPE = torch.bfloat16 if use_bf16 else torch.float16
print(f">> Using dtype: {DTYPE}")
MODEL_NAME = "baidu/Unlimited-OCR"
print(">> Downloading model (~6 GB for 3B params in BF16). First run takes a while...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModel.from_pretrained(
   MODEL_NAME,
   trust_remote_code=True,
   use_safetensors=True,
   torch_dtype=DTYPE,
)
model = model.eval().cuda()
print(">> Model loaded and moved to GPU.")

from PIL import Image, ImageDraw, ImageFont
import textwrap
os.makedirs("inputs", exist_ok=True)
os.makedirs("outputs/single_gundam", exist_ok=True)
os.makedirs("outputs/single_base", exist_ok=True)
os.makedirs("outputs/multi_page", exist_ok=True)
def load_font(size):
   for path in [
       "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
       "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
   ]:
       if os.path.exists(path):
           return ImageFont.truetype(path, size)
   return ImageFont.load_default()
def make_sample_page(path, page_no):
   W, H = 1240, 1754
   img = Image.new("RGB", (W, H), "white")
   d = ImageDraw.Draw(img)
   title_f, head_f, body_f = load_font(48), load_font(34), load_font(26)
   d.text((80, 70), f"Quarterly Operations Report — Page {page_no}",
          fill="black", font=title_f)
   d.line([(80, 145), (W - 80, 145)], fill="black", width=3)
   body = (
       "This document demonstrates Unlimited-OCR's one-shot long-horizon "
       "parsing. The model reads an entire page — headings, paragraphs, "
       "and tables — and emits structured text in a single decoding pass. "
       "Unlike classic OCR pipelines, no separate layout-analysis stage "
       "is required."
   )
   y = 190
   for line in textwrap.wrap(body, width=72):
       d.text((80, y), line, fill="black", font=body_f)
       y += 40
   y += 30
   d.text((80, y), f"Table {page_no}: Regional Revenue (USD, millions)",
          fill="black", font=head_f)
   y += 60
   rows = [
       ["Region",  "Q1",   "Q2",   "Q3"],
       ["North",   "12.4", "13.1", "15.0"],
       ["South",   "9.8",  "10.2", "11.7"],
       ["East",    "14.3", "13.9", "16.2"],
       ["West",    "11.1", "12.5", "12.9"],
   ]
   col_w, row_h, x0 = 260, 56, 80
   for r, row in enumerate(rows):
       for c, cell in enumerate(row):
           x = x0 + c * col_w
           d.rectangle([x, y, x + col_w, y + row_h], outline="black", width=2)
           d.text((x + 14, y + 12), cell, fill="black", font=body_f)
       y += row_h
   y += 50
   footer = (
       f"Note {page_no}: Figures are illustrative. Multi-page mode stitches "
       "context across pages, so cross-page references remain coherent."
   )
   for line in textwrap.wrap(footer, width=72):
       d.text((80, y), line, fill="black", font=body_f)
       y += 40
   img.save(path)
   return path
IMAGE_PATH = make_sample_page("inputs/sample_page_1.png", 1)
PAGE_2     = make_sample_page("inputs/sample_page_2.png", 2)
PAGE_3     = make_sample_page("inputs/sample_page_3.png", 3)
print(f">> Sample pages written: {IMAGE_PATH}, {PAGE_2}, {PAGE_3}")
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 8))
plt.imshow(Image.open(IMAGE_PATH))
plt.axis("off")
plt.title("Input document (page 1)")
plt.show()

print("\n" + "=" * 76)
print("STEP 4: Single image — GUNDAM mode (tiled, high detail)")
print("=" * 76)
model.infer(
   tokenizer,
   prompt="<image>document parsing.",
   image_file=IMAGE_PATH,
   output_path="outputs/single_gundam",
   base_size=1024,
   image_size=640,
   crop_mode=True,
   max_length=32768,
   no_repeat_ngram_size=35,
   ngram_window=128,
   save_results=True,
)

print("\n" + "=" * 76)
print("STEP 5: Single image — BASE mode (single view, faster)")
print("=" * 76)
model.infer(
   tokenizer,
   prompt="<image>document parsing.",
   image_file=IMAGE_PATH,
   output_path="outputs/single_base",
   base_size=1024,
   image_size=1024,
   crop_mode=False,
   max_length=32768,
   no_repeat_ngram_size=35,
   ngram_window=128,
   save_results=True,
)

print("\n" + "=" * 76)
print("STEP 6: Multi-page / PDF parsing")
print("=" * 76)
import tempfile
import fitz
def pdf_to_images(pdf_path, dpi=300):
   """Rasterize every PDF page to a PNG; return the list of image paths."""
   doc = fitz.open(pdf_path)
   tmp_dir = tempfile.mkdtemp(prefix="pdf_ocr_")
   mat = fitz.Matrix(dpi / 72, dpi / 72)
   paths = []
   for i, page in enumerate(doc):
       out = os.path.join(tmp_dir, f"page_{i + 1:04d}.png")
       page.get_pixmap(matrix=mat).save(out)
       paths.append(out)
   doc.close()
   return paths
SAMPLE_PDF = "inputs/sample_doc.pdf"
pdf = fitz.open()
for p in [IMAGE_PATH, PAGE_2, PAGE_3]:
   img_doc = fitz.open(p)
   rect = img_doc[0].rect
   pdf_bytes = img_doc.convert_to_pdf()
   img_pdf = fitz.open("pdf", pdf_bytes)
   page = pdf.new_page(width=rect.width, height=rect.height)
   page.show_pdf_page(rect, img_pdf, 0)
pdf.save(SAMPLE_PDF)
pdf.close()
print(f">> Built sample PDF: {SAMPLE_PDF}")
page_images = pdf_to_images(SAMPLE_PDF, dpi=300)
print(f">> Rasterized {len(page_images)} pages")
model.infer_multi(
   tokenizer,
   prompt="<image>Multi page parsing.",
   image_files=page_images,
   output_path="outputs/multi_page",
   image_size=1024,
   max_length=32768,
   no_repeat_ngram_size=35,
   ngram_window=1024,
   save_results=True,
)

print("\n" + "=" * 76)
print("STEP 7: Saved outputs")
print("=" * 76)
TEXT_EXTS = {".txt", ".md", ".mmd", ".json"}
def show_outputs(root):
   print(f"\n--- {root} ---")
   if not os.path.isdir(root):
       print("  (no output directory found)")
       return
   for dirpath, _, files in os.walk(root):
       for fn in sorted(files):
           fp = os.path.join(dirpath, fn)
           size = os.path.getsize(fp)
           print(f"  {fp}  ({size:,} bytes)")
           if os.path.splitext(fn)[1].lower() in TEXT_EXTS:
               with open(fp, "r", encoding="utf-8", errors="replace") as f:
                   content = f.read()
               preview = content[:1500]
               print("  " + "-" * 60)
               print("\n".join("  | " + ln for ln in preview.splitlines()))
               if len(content) > 1500:
                   print(f"  | ... [{len(content) - 1500:,} more chars]")
               print("  " + "-" * 60)
for out_dir in ["outputs/single_gundam", "outputs/single_base", "outputs/multi_page"]:
   show_outputs(out_dir)
print("""
============================================================================
DONE — CHEAT SHEET
============================================================================
Single image, dense/small text .... infer(), gundam (640 + crop_mode=True)
Single image, clean print ......... infer(), base   (1024, crop_mode=False)
Multi-page or PDF ................. infer_multi(), image_size=1024,
                                    ngram_window=1024
Long documents .................... keep max_length=32768 and the
                                    no_repeat_ngram settings — they prevent
                                    degeneration on long outputs.
Your own files .................... upload via Colab sidebar, point
                                    image_file / pdf_to_images() at them.
============================================================================
""")