On this tutorial, we construct a whole workflow for operating Baidu’s Limitless-OCR mannequin on doc photographs and multi-page PDFs. We configure the GPU setting, set up the required dependencies, load the 3B-parameter vision-language mannequin with automated choice of bfloat16 or float16, and generate structured pattern paperwork for testing. We then consider each the tiled Gundam inference mode and the quicker Base mode for single-page OCR earlier than extending the pipeline to multi-page PDF parsing with PyMuPDF and infer_multi(). All through the workflow, we protect long-context technology settings, repetition controls, and structured output dealing with to course of dense layouts, tables, paragraphs, and cross-page content material in a reproducible end-to-end pipeline.
import subprocess, sys
def pip_install(*pkgs):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *pkgs])
print(">> Putting in dependencies (1-2 min)...")
pip_install(
"transformers==4.57.1",
"Pillow",
"matplotlib",
"einops",
"addict",
"easydict",
"pymupdf",
"psutil",
"speed up",
)
print(">> Finished.")
import os
import torch
from transformers import AutoModel, AutoTokenizer
assert torch.cuda.is_available(), (
"No GPU detected! In Colab: Runtime -> Change runtime sort -> 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">> Utilizing dtype: {DTYPE}")
MODEL_NAME = "baidu/Limitless-OCR"
print(">> Downloading mannequin (~6 GB for 3B params in BF16). First run takes some time...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
mannequin = AutoModel.from_pretrained(
MODEL_NAME,
trust_remote_code=True,
use_safetensors=True,
torch_dtype=DTYPE,
)
mannequin = mannequin.eval().cuda()
print(">> Mannequin loaded and moved to GPU.")
We set up the required libraries and put together the Google Colab setting for Limitless-OCR inference. We confirm {that a} CUDA-enabled GPU is on the market and mechanically select bfloat16 or float16 based mostly on {hardware} assist. We then load the tokenizer and the 3B-parameter mannequin from Hugging Face, change them to analysis mode, and transfer them to the GPU.
from PIL import Picture, 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(dimension):
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, dimension)
return ImageFont.load_default()
def make_sample_page(path, page_no):
W, H = 1240, 1754
img = Picture.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.textual content((80, 70), f"Quarterly Operations Report — Web page {page_no}",
fill="black", font=title_f)
d.line([(80, 145), (W - 80, 145)], fill="black", width=3)
physique = (
"This doc demonstrates Limitless-OCR's one-shot long-horizon "
"parsing. The mannequin reads a whole web page — headings, paragraphs, "
"and tables — and emits structured textual content in a single decoding move. "
"Not like basic OCR pipelines, no separate layout-analysis stage "
"is required."
)
y = 190
for line in textwrap.wrap(physique, width=72):
d.textual content((80, y), line, fill="black", font=body_f)
y += 40
y += 30
d.textual content((80, y), f"Desk {page_no}: Regional Income (USD, tens of 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], define="black", width=2)
d.textual content((x + 14, y + 12), cell, fill="black", font=body_f)
y += row_h
y += 50
footer = (
f"Observe {page_no}: Figures are illustrative. Multi-page mode stitches "
"context throughout pages, so cross-page references stay coherent."
)
for line in textwrap.wrap(footer, width=72):
d.textual content((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">> Pattern pages written: {IMAGE_PATH}, {PAGE_2}, {PAGE_3}")
import matplotlib.pyplot as plt
plt.determine(figsize=(6, 8))
plt.imshow(Picture.open(IMAGE_PATH))
plt.axis("off")
plt.title("Enter doc (web page 1)")
plt.present()
We create the required enter and output directories and generate three practical pattern doc pages with PIL. We add headings, paragraphs, tables, and footnotes to check the mannequin on structured, layout-rich content material. We additionally preview the primary generated web page with Matplotlib earlier than sending it to the OCR pipeline.
print("n" + "=" * 76)
print("STEP 4: Single picture — GUNDAM mode (tiled, excessive element)")
print("=" * 76)
mannequin.infer(
tokenizer,
immediate="doc 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,
)
We run single-image OCR utilizing Gundam mode, which mixes a worldwide doc view with tiled picture crops. We allow crop_mode and use a smaller tile dimension to protect advantageous textual content and enhance recognition on dense doc layouts. We additionally configure long-output technology and repetition controls to make sure the mannequin produces secure, structured outcomes.
print("n" + "=" * 76)
print("STEP 5: Single picture — BASE mode (single view, quicker)")
print("=" * 76)
mannequin.infer(
tokenizer,
immediate="doc 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,
)
We course of the identical doc utilizing Base mode with a single 1024-pixel picture view. We flip off picture cropping to scale back inference complexity and enhance processing pace for clear, clearly printed pages. We retain the identical output size and repetition-control settings to instantly evaluate Base mode with Gundam mode.
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 each PDF web page to a PNG; return the checklist of picture paths."""
doc = fitz.open(pdf_path)
tmp_dir = tempfile.mkdtemp(prefix="pdf_ocr_")
mat = fitz.Matrix(dpi / 72, dpi / 72)
paths = []
for i, web page in enumerate(doc):
out = os.path.be a part of(tmp_dir, f"page_{i + 1:04d}.png")
web page.get_pixmap(matrix=mat).save(out)
paths.append(out)
doc.shut()
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)
web page = pdf.new_page(width=rect.width, top=rect.top)
web page.show_pdf_page(rect, img_pdf, 0)
pdf.save(SAMPLE_PDF)
pdf.shut()
print(f">> Constructed pattern PDF: {SAMPLE_PDF}")
page_images = pdf_to_images(SAMPLE_PDF, dpi=300)
print(f">> Rasterized {len(page_images)} pages")
mannequin.infer_multi(
tokenizer,
immediate="Multi web 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,
)
We create a three-page PDF from the generated doc photographs and rasterize every web page of the PDF right into a high-resolution PNG utilizing PyMuPDF. We move the ensuing page-image sequence to infer_multi() in order that the mannequin can parse the whole doc in a single long-horizon inference operation. We additionally widen the n-gram repetition window to keep up secure decoding throughout a number of pages.
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 listing discovered)")
return
for dirpath, _, information in os.stroll(root):
for fn in sorted(information):
fp = os.path.be a part of(dirpath, fn)
dimension = os.path.getsize(fp)
print(f" {fp} ({dimension:,} bytes)")
if os.path.splitext(fn)[1].decrease() in TEXT_EXTS:
with open(fp, "r", encoding="utf-8", errors="change") as f:
content material = f.learn()
preview = content material[:1500]
print(" " + "-" * 60)
print("n".be a part of(" | " + ln for ln in preview.splitlines()))
if len(content material) > 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 picture, dense/small textual content .... infer(), gundam (640 + crop_mode=True)
Single picture, clear print ......... infer(), base (1024, crop_mode=False)
Multi-page or PDF ................. infer_multi(), image_size=1024,
ngram_window=1024
Lengthy paperwork .................... hold max_length=32768 and the
no_repeat_ngram settings — they forestall
degeneration on lengthy outputs.
Your personal information .................... add through Colab sidebar, level
image_file / pdf_to_images() at them.
============================================================================
""")
We examine the output directories created by the single-page and multi-page inference runs. We checklist each generated file and show previews of supported textual content, Markdown, MMD, and JSON artifacts. We conclude the workflow with a concise reference summarizing the really helpful inference modes for dense photographs, clear pages, and multi-page PDFs.
In conclusion, we accomplished a sensible OCR pipeline that handles each high-detail single-page paperwork and lengthy multi-page PDFs inside Google Colab. We in contrast Gundam and Base inference modes, rasterized PDFs into model-ready web page photographs, ran long-horizon doc parsing, and inspected the generated textual content, Markdown, and auxiliary artifacts instantly from the output directories. We additionally configured the workflow to adapt to completely different GPU capabilities whereas retaining the technology parameters required for secure long-document decoding. It gives us with a reusable basis for making use of Limitless-OCR to experiences, scanned types, technical paperwork, tables, and different layout-rich content material with out counting on a separate conventional OCR and layout-analysis stack.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree scholar at IIT Madras, is obsessed with making use of know-how and AI to deal with real-world challenges. With a eager curiosity in fixing sensible issues, he brings a recent perspective to the intersection of AI and real-life options.
