LaTeX OCR Guide

A skill for extracting mathematical formulas from images, PDFs, and handwritten notes and converting them to LaTeX code. Covers tool selection, batch processing workflows, and quality verification techniques.

Tool Landscape

Available Math OCR Tools

Tool	Type	Accuracy	Best For	License
Mathpix	Cloud API	Very high	All math, diagrams	Commercial ($)
LaTeX-OCR (Lukas Blecher)	Local model	High	Printed formulas	MIT
Pix2Tex	Local model	High	Single equations	MIT
Nougat (Meta)	Local model	High	Full papers with math	MIT
InftyReader	Desktop	High	Printed math, Japanese	Commercial
img2latex	Local model	Moderate	Simple equations	MIT

Quick Start with LaTeX-OCR

# Install the open-source LaTeX-OCR package
pip install "pix2tex[gui]"

# Or install from GitHub for latest version
pip install git+https://github.com/lukas-blecher/LaTeX-OCR.git

from pix2tex.cli import LatexOCR
from PIL import Image

def recognize_formula(image_path: str) -> str:
    """
    Convert a formula image to LaTeX code.

    Args:
        image_path: Path to image containing a mathematical formula
    Returns:
        LaTeX string representation of the formula
    """
    model = LatexOCR()
    img = Image.open(image_path)
    latex_code = model(img)
    return latex_code

# Single image
result = recognize_formula('formula.png')
print(result)
# Output: E = mc^{2}

Batch Processing Workflow

Processing Multiple Formulas from a PDF

import fitz  # PyMuPDF
from PIL import Image
import io

def extract_formulas_from_pdf(pdf_path: str, output_dir: str,
                                min_height: int = 30) -> list[dict]:
    """
    Extract formula regions from a PDF and convert to LaTeX.

    Args:
        pdf_path: Path to the PDF file
        output_dir: Directory to save extracted formula images
        min_height: Minimum height (px) to consider as formula region
    """
    doc = fitz.open(pdf_path)
    model = LatexOCR()
    results = []

    for page_num in range(len(doc)):
        page = doc[page_num]
        # Extract images from page
        image_list = page.get_images(full=True)

        for img_idx, img_info in enumerate(image_list):
            xref = img_info[0]
            pix = fitz.Pixmap(doc, xref)

            if pix.height >= min_height:
                img_data = pix.tobytes("png")
                img = Image.open(io.BytesIO(img_data))

                try:
                    latex = model(img)
                    results.append({
                        'page': page_num + 1,
                        'image_index': img_idx,
                        'latex': latex,
                        'confidence': 'high' if len(latex) > 3 else 'low'
                    })
                except Exception as e:
                    results.append({
                        'page': page_num + 1,
                        'image_index': img_idx,
                        'latex': None,
                        'error': str(e)
                    })

    return results

Processing Handwritten Notes

For handwritten mathematics, preprocessing improves accuracy significantly:

import cv2
import numpy as np

def preprocess_handwritten(image_path: str) -> Image.Image:
    """
    Preprocess a handwritten formula image for better OCR accuracy.
    """
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

    # 1. Denoise
    img = cv2.fastNlMeansDenoising(img, h=10)

    # 2. Adaptive thresholding for varying illumination
    img = cv2.adaptiveThreshold(
        img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY, 15, 8
    )

    # 3. Dilation to connect broken strokes
    kernel = np.ones((2, 2), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)

    # 4. Crop to content with padding
    coords = cv2.findNonZero(255 - img)
    x, y, w, h = cv2.boundingRect(coords)
    pad = 20
    img = img[max(0, y-pad):y+h+pad, max(0, x-pad):x+w+pad]

    return Image.fromarray(img)

Using Mathpix API

Pricing note: Mathpix is a paid service (starting at $5/month). For free open-source alternatives, use pix2tex/LaTeX-OCR or Nougat (Meta), both MIT-licensed and capable of running locally.

For production-quality results, the Mathpix API provides the highest accuracy:

import requests
import base64

def mathpix_ocr(image_path: str, app_id: str, app_key: str) -> dict:
    """
    Use Mathpix API for high-accuracy math OCR.
    """
    with open(image_path, 'rb') as f:
        image_data = base64.b64encode(f.read()).decode()

    response = requests.post(
        'https://api.mathpix.com/v3/text',
        headers={
            'app_id': app_id,
            'app_key': app_key,
            'Content-type': 'application/json'
        },
        json={
            'src': f'data:image/png;base64,{image_data}',
            'formats': ['latex_styled', 'text'],
            'data_options': {'include_asciimath': True}
        }
    )
    return response.json()

Verification and Correction

Always verify OCR output by rendering the LaTeX:

import matplotlib.pyplot as plt

def verify_latex(latex_string: str, output_path: str = 'verify.png'):
    """Render LaTeX formula and save as image for visual verification."""
    fig, ax = plt.subplots(figsize=(8, 2))
    ax.text(0.5, 0.5, f'${latex_string}$', fontsize=20,
            ha='center', va='center', transform=ax.transAxes)
    ax.axis('off')
    fig.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    print(f"Verification image saved to {output_path}")

Common OCR errors to watch for: confusing

l

with

1

,

O

with

0

, missing superscripts/subscripts, incorrect fraction nesting, and misrecognized Greek letters. Always proofread critical equations before submission.