Who is the ai behind this online ocr tool - https://textgens.com/ocr-tool/
I understand that it may be fine-tuned model. Can it be deploy locally on 12gb vram rtx3060?
Chances are low but I have to ask anyway …
sir try, deepseek ocr i have used it its good btw here is my tele lets connect computer geek here -
Humanloved
Hardware doesn’t say no anymore. The OCR scene flipped six months ago.
Reading you back:
Q1 — Who’s behind textgens?
Q2 — Is it fine-tuned?
Q3 — Will it run on a 3060 12GB?
And the bigger thing nobody asks out loud:
“Am I just renting the magic from a website forever, or can I have it sitting in my closet?”
The answer to that real question is the same as the answer to Q3.
The three answers, scan-fast
| Answer | |
|---|---|
| Q1 | One-person SaaS wrapper. [email protected], 7 sister sites in the footer, WordPress, +07:00 timezone. Almost certainly fronting Mistral OCR’s free tier or Gemini 2.5 Flash. |
| Q2 | Not a fine-tune — just a wrapper. Nothing to download. What they run lives on Mistral’s servers. The 8-page limit is their throttle, not the API’s. |
| Q3 | Yes. And the model that fits is better than what they’re fronting. |
The flip
October 2025 dropped six open-source OCR models in one month.
The winner is 0.9 billion parameters — smaller than a basic chat LLM.
It beats GPT-5.4, Mistral OCR, and Gemini 2.5 Pro on accuracy.
Minimum GPU Baidu officially tested it on:
RTX 3060 12GB. Your exact card.
Meet PaddleOCR-VL-1.5
| Model | PaddleOCR-VL-1.5-0.9B |
| Min GPU | RTX 3060 12GB — Baidu’s FAQ confirms it |
| Score | 94.5% on OmniDocBench v1.5 — beats every cloud API |
| Languages | 109 (Hindi, Tamil, Bengali, Punjabi via fine-tune) |
| License | Apache 2.0 |
| Cost / 1000 pages | ~$0.09 electricity vs $1 for Mistral API = 167× cheaper |
| Size | 0.9B — specialization beat scale |
A 0.9B model beating 200B+ general models at one specific task. Doesn’t sound real until you remember Baidu trained it only for documents instead of trying to make it a generalist.
The one trap — and the fix
Default model-card snippet used vanilla attention.
One user’s first run: VRAM ballooned to 45GB on a single page. Two minutes per page. Looks like the 3060 just died — it didn’t.
Same user, after switching to FlashAttention 2 (smarter math, same answer, way less memory):
3.3GB VRAM. 19 seconds per page.
Source: HF discussion #59. README’s been patched. Use the current one, not any tutorial older than November 2025.
Setup that works on a 3060 — copy-paste safe, 15 minutes
Install order matters. FlashAttention prebuilt wheel BEFORE vLLM — reverse the order and the build breaks every time. Reference: Alex Zhang’s walkthrough.
# Fresh venv (don't pollute your global Python)
python -m venv .venv_ocr && source .venv_ocr/bin/activate
# PaddleOCR with doc-parser
pip install "paddleocr[doc-parser]"
# FlashAttention prebuilt wheel — THIS before vLLM
pip install https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.3.14/flash_attn-2.8.2+cu128torch2.8-cp310-cp310-linux_x86_64.whl
# vLLM via PaddleOCR's installer
paddleocr install_genai_server_deps vllm
# Launch — NOTE the 0.3 not 0.8
paddleocr genai_server \
--model_name PaddleOCR-VL-1.5-0.9B \
--backend vllm \
--port 8118 \
--backend_config <(echo -e 'gpu-memory-utilization: 0.3')
Two more if-then signs:
If it OOMs on launch → drop
gpu-memory-utilizationfrom 0.3 to 0.2. The official FAQ names 0.3 as the 12GB sweet spot, but dense table pages push past it.
If FlashAttention wheel install fails → wrong build for your stack. The URL above is
cu128 + torch2.8 + python3.10. Match yours to a different wheel from the same releases page.
Calling the running server from Python:
from paddleocr import PaddleOCRVL
pipeline = PaddleOCRVL(
vl_rec_backend="vllm-server",
vl_rec_server_url="http://127.0.0.1:8118/v1"
)
output = pipeline.predict("your_document.pdf")
for res in output:
res.save_to_markdown(save_path="output")
Three backups if PaddleOCR-VL doesn’t fit your specific workflow:
| Model | Why pick this | Link |
|---|---|---|
| olmOCR-2-7B | Allen AI, ties GPT-5 & Gemini 2.5 Pro on handwriting | arxiv |
| DeepSeek-OCR 3B | Smaller, faster, end-to-end | HF roundup |
| Multimodal-OCR UI | Loads 5 models side-by-side for A/B testing | repo |
Honest counter — read before going local: on sococrbench (independent multilingual handwriting test), GPT-5 and Gemini 2.5 Pro still beat the open-source crowd. PaddleOCR-VL wins on clean printed text + document structure. For grandma’s cursive letters or messy mixed-script archives, cloud might still edge ahead. Pick honestly based on YOUR documents.
be-creative
Bonus loot drop 
Workarounds you can retire today
Hope the cloud free-tier doesn’t get patched→ local has no rate limit, no patch riskRun Tesseract because it’s “the open-source OCR”→ 2026 models are 3-5× more accurateAccept the 8-page limit on textgens→ local processes the whole batch in one runWait until 24GB GPUs are affordable→ 12GB is enough now; the 0.9B fits with room
The 167× cost flip — the math
| Backend | Price per 1000 pages |
|---|---|
| GPT-5.4 vision | ~$15 |
| Mistral OCR API | $1 |
| Your 3060 + PaddleOCR-VL | ~$0.09 electricity |
Source: CodeSOTA leaderboard economics page.
The October-2025 wave that flipped the field
Six open-source OCR models in one month. Most “best OCR 2025” listicles still recommend Tesseract.
- Nanonets OCR2-3B
- PaddleOCR-VL-0.9B ← the winner
- DeepSeek-OCR-3B
- Chandra-OCR-8B
- olmOCR-2-7B
- LightOnOCR-1B
All open-source. All fit on consumer GPUs.
Side door for Apple Silicon owners reading this
Same model deploys via MLX-VLM on M-series Macs. The 12GB-VRAM constraint becomes “16GB unified memory” on Mac. Same answer, different door.
textgens’s sister sites, ranked by parallel-free-quota usefulness
If you need free OCR right now while setting up local, the same operator runs six more wrappers on different upstream APIs. Parallel free-quota pools.
| Sister site | Likely wraps |
|---|---|
| anythingtranslate.com | Gemini or DeepL |
| anytranscribe.com | Whisper |
| anyvoicelab.com | ElevenLabs free tier |
| onlinechatbot.ai | OpenAI / Claude |
| dailysimulator.com, fontadvice.com, colorany.com | Niche LLM wrappers |
You came in expecting the polite no.
A year ago it would’ve been right.
Six open-source models that fit your card overtook the cloud APIs in October 2025.
Most “best OCR” listicles haven’t noticed.
You said chances were low.
Chances aren’t low — they flipped.
The 3060 was waiting.
i wanted to swear and say f%$#ing thank you … but politely, thank you 
You can also try OCR space. It’s not AI, but their APIs’ free tier offers 25,000 requests per month. However, their PDF upload limit is 3 pages.
!