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They constantly measure DomRect using javascript, which is a unique hardware-based metric that can be used to track individual users.

Imagine the cost of running duck.ai. What exactly is the revenue that it brings in?

Of course, if it were some honeypot, using DomRects to track users (and DomRect is not protected by Tor Browser or Mullvad Browser etc), well then it doesn’t really matter if it’s not bringing in much revenue since it’s value is in being a honeypot.

Yes, DomRect can be used legitimately in coding without tracking users… but why does ddg need to use this when they know that it CAN be used to track users and users have no way to audit the servers?

It’s really interesting they measure DomRect and not Canvas when privacy-aware users often block canvas fingerprinting but don’t block DomRect.

It’s sus

It’s a honeypot, so of course making it so anyone can use it, even without a phone number, even with a mobile device, is not a priority.

I can’t fathom that Signal is not a honeypot.

Back when I tried to register, not only did they want a phone number (which usually links to IRL KYC stuff) but they also wanted me to complete a google captcha that took different metrics (canvas, etc) of my device.

Why is that needed? They say it’s to reduce spam, I just don’t believe it it.

Not only that, I can’t register using a linux system. I simply MUST register with a mobile device (that I will likely have on me) that can potentially track me through the cellular modem in the device and also likely has listening devices inside the device and a camera attached that is very hard to cover (because it’s embedded into the glass and and covering it with anything messes with the swipe up function).

No organization would create something that is so incredibly hostile to people who don’t want mobile phones and don’t want numbers unless they were a honeypot. I even think that Signal was created in a large part to try to siphon popularity away from XMPP before it could reach mass adoption.

I don’t understand what venv is or why this would work better. Will this make the compatibility issues go away? I could also just create a virtual Ubuntu environment that’s fresh if that would be easier and try to give that environment access to my GPU but I don’t know if that would work.

because it’s encrypted by https and my VPN has a decent reputation. Yes, it’s possible that the VPN is secretly selling everything they can to big data, but if it were exposed then it would ruin their entire business.

That’s exactly what I am trying to do, I’m just not that sure how to do it. I have the hardware needed, I just need to set up a docker with PyTorch and then find a way to set up Gradio inside that and then add TrOCR from hugging face, and then I’m good. I just am not totally sure how to do that and it seems hard, and when I ask AI for advice, it often is like “just run the following” and it’s wrong, and I’m not skilled enough to know why.

I previously tried to commit suicide when I was younger and was involuntarily hospitalized. As a result I’m a prohibited person. I can’t legally buy a firearm. This makes it harder for me to protect myself if things get worse. I don’t know if voting in my liberal community is going to change anything.

I don’t trust big tech to not extract data and metadata and save it. Many companies get served with government requests to save data and keep it secret. Even if handwritingocr.com doesn’t have such an agreement, it could run on AWS and that has an agreement. I would much rather do this locally. Some of the writings are confidential. Handwritingocr.com says data is encrypted in transit and at rest, but it’s not open source and even if it were I can’t verify the server code.

also Tesseract is CPU only, right? It will be so slow.

So try again… in a couple of hours…

Why would that make a difference? It’s a local model right?

I’m pragmatic. If a Tornado is coming towards me, I don’t try to rush into the eye to fight it. I change things I can, flee the things I can’t.

Terminal error after running GPT code:

python3 trocr_pdf.py small.pdf output.txt
Traceback (most recent call last):
  File "/home/user/.local/lib/python3.10/site-packages/transformers/utils/hub.py", line 479, in cached_files
    hf_hub_download(
  File "/home/user/.local/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "/home/user/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1007, in hf_hub_download
    return _hf_hub_download_to_cache_dir(
  File "/home/user/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1124, in _hf_hub_download_to_cache_dir
    os.makedirs(os.path.dirname(blob_path), exist_ok=True)
  File "/usr/lib/python3.10/os.py", line 215, in makedirs
    makedirs(head, exist_ok=exist_ok)
  File "/usr/lib/python3.10/os.py", line 225, in makedirs
    mkdir(name, mode)
PermissionError: [Errno 13] Permission denied: '/home/user/.cache/huggingface/hub/models--microsoft--trocr-base-handwritten'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/user/Documents/trocr_pdf.py", line 39, in <module>
    main(pdf_path, out_path)
  File "/home/user/Documents/trocr_pdf.py", line 11, in main
    processor = TrOCRProcessor.from_pretrained(model_name)
  File "/home/user/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1394, in from_pretrained
    args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
  File "/home/user/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1453, in _get_arguments_from_pretrained
    args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
  File "/home/user/.local/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py", line 489, in from_pretrained
    raise initial_exception
  File "/home/user/.local/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py", line 476, in from_pretrained
    config_dict, _ = ImageProcessingMixin.get_image_processor_dict(
  File "/home/user/.local/lib/python3.10/site-packages/transformers/image_processing_base.py", line 333, in get_image_processor_dict
    resolved_image_processor_files = [
  File "/home/user/.local/lib/python3.10/site-packages/transformers/image_processing_base.py", line 337, in <listcomp>
    resolved_file := cached_file(
  File "/home/user/.local/lib/python3.10/site-packages/transformers/utils/hub.py", line 322, in cached_file
    file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
  File "/home/user/.local/lib/python3.10/site-packages/transformers/utils/hub.py", line 524, in cached_files
    raise OSError(
OSError: PermissionError at /home/user/.cache/huggingface/hub/models--microsoft--trocr-base-handwritten when downloading microsoft/trocr-base-handwritten. Check cache directory permissions. Common causes: 1) another user is downloading the same model (please wait); 2) a previous download was canceled and the lock file needs manual removal.

LLMs are so bad at code sometimes. This happens all the time time with LLMs and code for me, the code is unusable and it saves no time because it’s a rabbit hole leading to nowhere.

I also don’t know if this is the right approach to the problem. Any sort of GUI interface would be easier. This is also hundreds of pages of handwritten stuff I want to change to text.

that’s not for TrOCR, it’s just for OCR, which may not work for handwriting

I did try some of the GPT steps:

pip install --upgrade transformers pillow pdf2image

getting some errors:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 3/4 [transformers]  WARNING: The scripts transformers and transformers-cli are installed in '/home/user/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
mistral-common 1.5.2 requires pillow<11.0.0,>=10.3.0, but you have pillow 12.1.0 which is incompatible.
moviepy 2.1.2 requires pillow<11.0,>=9.2.0, but you have pillow 12.1.0 which is incompatible.

this is what GPT said to run, but it makes no sense because I don’t have TrOCR even downloaded or running at all.

Install packages: pip install --upgrade transformers pillow pdf2image
Ensure poppler is installed:

Ubuntu/Debian: sudo apt install -y poppler-utils
macOS: brew install poppler

Execute: python3 trocr_pdf.py input.pdf output.txt

That’s the script to save and run.

#!/usr/bin/env python3
import sys
from pdf2image import convert_from_path
from PIL import Image
import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

def main(pdf_path, out_path="output.txt", dpi=300):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model_name = "microsoft/trocr-base-handwritten"
    processor = TrOCRProcessor.from_pretrained(model_name)
    model = VisionEncoderDecoderModel.from_pretrained(model_name).to(device)

    pages = convert_from_path(pdf_path, dpi=dpi)
    results = []
    for i, page in enumerate(pages, 1):
        page = page.convert("RGB")
        # downscale if very large to avoid OOM
        max_dim = 1600
        if max(page.width, page.height) > max_dim:
            scale = max_dim / max(page.width, page.height)
            page = page.resize((int(page.width*scale), int(page.height*scale)), Image.Resampling.LANCZOS)

        pixel_values = processor(images=page, return_tensors="pt").pixel_values.to(device)
        generated_ids = model.generate(pixel_values, max_length=512)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        results.append(f"--- Page {i} ---\n{text.strip()}\n")

    with open(out_path, "w", encoding="utf-8") as f:
        f.write("\n".join(results))
    print(f"Saved OCR text to {out_path}")

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python3 trocr_pdf.py input.pdf [output.txt]")
        sys.exit(1)
    pdf_path = sys.argv[1]
    out_path = sys.argv[2] if len(sys.argv) > 2 else "output.txt"
    main(pdf_path, out_path)

I don’t remember exactly, but I have rocm 7.2 installed, and there was something I was trying to install inside pip for rocm and it just wouldn’t work, it was like 7.2 rocm wasn’t out or the link didn’t work. The LLM tried multiple suggestion and they all failed, then I gave up. When I said “inside” pip, I don’t know if that’s accurate. I am very knew to pip and am decent at linux and only know a small amount of coding and lack python familiarity.

Lots of people laughing in Germany in the 30s at people fleeing weren’t laughing in the 40s.

which community? i did look and didn’t see one.

I am happy to participate. I do not want my IP and ping data sold to data brokers to serve targeted ads, track me, and go to the police surveillance state. I’m sure you are a good citizen who always keeps location on and feels like life would be easier if everyone just complied while you proudly put ring cameras on every door. Not everyone is a tech bro neo-feudalist.

i got that a lot when i first started using linux-based distros. “why aren’t you typing man?” or “just type sudo rmdir /”

GPT 5 gave me a lot of code that returned errors. I really need help with the specific terminal code or knowing if I am even approaching the problem right.

I am trying to run an OCR program for handwriting to process some large PDFs of old journals that are scanned into PDF. Doing it by hand will take a very long time. I have a amd gpu and have rocm installed. I tried to configure pip with rocm and failed. I was considering pulling a docker of PyTorth and then configuring gradio in it, then trying to get gradio to run TrOCR. I have never run gradio. I have “easier” LLM programs like LM Studio and Ollama but I don’t know if they can run TrOCR. There is AMD documentation on running OCR (https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/inference/ocr_vllm.html) but it’s not clear if it works well with handwriting. TrOCR is just trained for handwriting. It’s also on huggingface, which i don’t know how to use that well.