---

name: ocr-inference description: "Guides pix2tex CLI and Python OCR inference from equation images to LaTeX, including images, checkpoints, CPU/CUDA choices, and prediction troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

OCR Inference

Use this sub-skill when the user wants LaTeX code from an equation image, wants to call pix2tex or LatexOCR, or needs to debug inference quality, model checkpoints, image preprocessing, or CLI flags.

Quick Route

1. For the Python class and helper signatures, read references/api-reference.md.
2. For CLI commands and flags, read references/cli-reference.md.
3. For image size/mode/preprocessing decisions, read references/image-preprocessing.md.
4. For common failures, read references/troubleshooting.md.
5. Before loading model weights, run scripts/check_pix2tex_cli.py and, for a local image, scripts/inspect_pix2tex_image.py.

Standard CLI Workflow

pix2tex --no-cuda path/to/equation.png

Use --no-cuda for CPU-only environments or deterministic smoke tests. Omit it only after verifying CUDA PyTorch in the target environment. Add --no-resize when you do not want the auxiliary image-resizer model to adjust image width. Use -t/--temperature to control sampling; lower values make repeated predictions less variable.

Standard Python Workflow

from PIL import Image
from pix2tex.cli import LatexOCR

model = LatexOCR()  # may download checkpoints if missing
latex = model(Image.open("equation.png"))
print(latex)

Instantiate LatexOCR() only when checkpoint downloads and compute are allowed. For CPU-only use, pass an argparse-like object or CLI route with no_cuda=True.

Boundaries

• For GUI screenshot workflows or API serving, use ../interactive-apps-and-api/SKILL.md.
• For dataset creation, formula rendering, or scraping, use ../data-preparation/SKILL.md.
• For training/evaluation or model architecture details, use ../training-and-evaluation/SKILL.md.