Startup Ideas Bank
Unrealistic Tech Wonderland with Glaring Execution Gaps
AI roast score: 55/100 (D)
The idea
baidu/Unlimited-OCR — Unlimited OCR Works: Welcome the Era of One-shot Long-horizon Parsing.
Unlimited OCR Works
Welcome the Era of One-shot Long-horizon Parsing.
Release
[2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces .
[2026/06/23] 📄 Our paper is now available on arXiv .
[2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope .
[2026/06/22] 🚀 We present Unlimited-OCR , aiming to push Deepseek-OCR one step further.
Inference
Transformers
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9：
torch==2.10.0
torchvision==0.25.0
transformers==4.57.1
Pillow==12.1.1
matplotlib==3.10.8
einops==0.8.2
addict==2.4.0
easydict==1.13
pymupdf==1.27.2.2
psutil==7.2.2
import os
import torch
from transformers import AutoModel , AutoTokenizer
model_name = 'baidu/Unlimited-OCR'
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 = torch . bfloat16 ,
)
model = model . eval (). cuda ()
# ── Single image supports two configs: gundam or base ──
# gundam: base_size=1024, image_size=640, crop_mode=True
# base: base_size=1024, image_size=1024, crop_mode=False
model . infer (
tokenizer ,
prompt = '<image>document parsing.' ,
image_file = 'your_image.jpg' ,
output_path = 'your/output/dir' ,
base_size = 1024 , image_size = 640 , crop_mode = True ,
max_length = 32768 ,
no_repeat_ngram_size = 35 , ngram_window = 128 ,
save_results = True ,
)
# ── Multi page / PDF only uses base (image_size=1024) ──
model . infer_multi (
tokenizer ,
prompt = '<image>Multi page parsing.' ,
image_files = [ 'page1.png' , 'page2.png' , 'page3.png' ],
output_path = 'your/output/dir' ,
image_size = 1024 ,
max_length = 32768 ,
no_repeat_ngram_size = 35 , ngram_window = 1024 ,
save_results = True ,
)
# ── PDF (convert pages to images, then multi-page parsing) ──
import tempfile , fitz # PyMuPDF
def pdf_to_images ( pdf_path , dpi = 300 ):
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
model . infer_multi (
tokenizer ,
prompt = '<image>Multi page parsing.' ,
image_files = pdf_to_images ( 'your_doc.pdf' , dpi = 300 ),
output_path = 'your/output/dir' ,
image_size = 1024 ,
max_length = 32768 ,
no_repeat_n
The roast
Your pitch reads more like a sci-fi novel than a viable startup. The technology sounds impressive, but without clear market validation or a concrete go-to-market strategy, it's just fantasy. Your target market of enterprise buyers (q4=enterprise) demands proven ROI, not untested dreams. Plus, being a solo founder (q13=solo) without funding (q14=no_funding) raises serious doubts about your ability to execute this vision. Three red flags stand out: your focus on unproven futuristic tech, the lack of a clear business model, and the significant execution risks given your solo status.
Red flags
- Unproven futuristic technology
- No clear business model
- Significant execution risks as a solo founder
Verdict
Your tech might be impressive on paper, but without market validation and a realistic execution plan, this is doomed to fail.
Roast your own startup idea →