--- name: latex-ocr-guide-342c66933b15 description: "Extract and convert mathematical formulas from images and PDFs to LaTeX code" metadata: openclaw: emoji: "🔍" category: "tools" subcategory: "ocr-translate" keywords: ["math OCR", "formula recognition", "LaTeX OCR", "document OCR", "equation extraction"] source: "wentor" --- # 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 ```bash # 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 ``` ```python 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 ```python 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: ```python 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 $5/month). For free open-source alternatives, use pix2tex/LaTeX-OCR or Nougat (Meta), both MIT-licensed and capable of running locally. For production-quality results, the Mathpix API provides the highest accuracy: ```python import requests import base64 def mathpix_ocr(image_path: str, app_id: str, app_key: str) -> dict: """ Use Mathpix API for high-accuracy math OCR. """ with open(image_path, 'rb') as f: image_data = base64.b64encode(f.read()).decode() response = requests.post( 'https://api.mathpix.com/v3/text', headers={ 'app_id': app_id, 'app_key': app_key, 'Content-type': 'application/json' }, json={ 'src': f'data:image/png;base64,{image_data}', 'formats': ['latex_styled', 'text'], 'data_options': {'include_asciimath': True} } ) return response.json() ``` ## Verification and Correction Always verify OCR output by rendering the LaTeX: ```python import matplotlib.pyplot as plt def verify_latex(latex_string: str, output_path: str = 'verify.png'): """Render LaTeX formula and save as image for visual verification.""" fig, ax = plt.subplots(figsize=(8, 2)) ax.text(0.5, 0.5, f'${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. --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/ocr-translate/latex-ocr-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/ocr-translate/latex-ocr-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Design, Education & Research Roles: Data Scientist, Researcher, UX Designer Specialties: API Tools: Fitz, Pix2Tex Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: de5c5745d9270dc0da281a727e09f01762a071a404035b5963a4441c93576b09