https://github.com/sped0n/texifast

Fast LaTeX OCR powered by texify, but without bloated dependencies like torch or transformers.
https://github.com/sped0n/texifast

latex-ocr ocr onnxruntime texify

Last synced: 4 months ago
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Fast LaTeX OCR powered by texify, but without bloated dependencies like torch or transformers.

• Host: GitHub
• URL: https://github.com/sped0n/texifast
• Owner: Sped0n
• License: mit
• Created: 2024-10-11T16:52:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-22T12:03:51.000Z (over 1 year ago)
• Last Synced: 2025-12-01T22:11:58.249Z (6 months ago)
• Topics: latex-ocr, ocr, onnxruntime, texify
• Language: Python
• Homepage: https://pypi.org/project/texifast/
• Size: 1.06 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# texifast ![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2FSped0n%2Ftexifast%2Fmain%2Fpyproject.toml) ![PyPI - Version](https://img.shields.io/pypi/v/texifast) ![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/Sped0n/texifast/pytest.yml?label=pytest)

LaTeX and markdown OCR powered by [texify](https://github.com/VikParuchuri/texify), without bloated dependencies like torch or transformers.

## Features

- Minimal dependency graph

- Compared to [Optimum](https://github.com/huggingface/optimum), texifast is faster (~20%) and has a smaller memory footprint (~20%). For details, see [benchmark](https://github.com/Sped0n/texifast/tree/main/benchmark).

- Supports IOBinding features of ONNXRuntime and optimizes for CUDAExecutionProvider.

- Supports quantized/mixed precision models.

## Installation

You must implicitly specify the required dependencies.

```

pip install texifast[cpu]

# or if you want to use CUDAExecutionProvider

pip install texifast[gpu]

```

> ⚠️⚠️⚠️

>

> **Do not install with** `pip install texifast` **!!!**

## Quickstart

This quick start use the [image in test folder](https://raw.githubusercontent.com/Sped0n/texifast/main/tests/latex.png), you can use whatever you like.

```python

from texifast.model import TxfModel

from texifast.pipeline import TxfPipeline

model = TxfModel(

    encoder_model_path="./encoder_model_quantized.onnx",

    decoder_model_path="./decoder_model_merged_quantized.onnx",

)

texifast = TxfPipeline(model=model, tokenizer="./tokenizer.json")

print(texifast("./latex.png"))

```

> You can download the quantized ONNX model [here](https://huggingface.co/Spedon/texify-quantized-onnx/tree/main) and the FP16 ONNX model [here](https://huggingface.co/Spedon/texify-fp16-onnx/tree/main).

## API

The full Python API documentation can be found [here](https://github.com/Sped0n/texifast/tree/main/docs).

## Credits

- https://github.com/VikParuchuri/texify

- https://github.com/MosRat/MixTex-rs

- https://github.com/xenova/transformers.js

- https://onnxruntime.ai/docs/api/python/api_summary.html

- https://github.com/ml-tooling/lazydocs