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SKILL.md
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.
