Academic-grade Inference

High-performance, private, and localized optical character recognition framework optimized for archival language preservation.

Installation

Deploy the engine using our official package manager. We recommend virtual environments for research isolation.

Bash

$ pip install monocr

Initialize the engine and run an inference pass on a local manuscript image.

example.py

# Initialize engine
from monocr import MonOCR

ocr = MonOCR()
text = ocr.predict('manuscript.jpg')
print(text)

Optimization parameters for maximum character recognition accuracy.

300 DPI baseline. 600 DPI recommended for ancient archives.

Diffuse lighting required. Minimize specular reflections.

Cross-platform integration libraries for academic research.

monocr


												// Node.js Implementation
import { MonOCR } from 'monocr';

const ocr = new MonOCR();
const text = await ocr.predict('page.jpg');

Batch processing for historical archives.

Terminal

# Process single file
monocr read manuscript_01.jpg

# Batch process archive directory
monocr batch ./scans/ --output results.txt

Local recognition guarantee. Data integrity is maintained on-device. No manuscript information ever leaves your environment.

Open-source governance for research and preservation project. You are free to use and modify the engine for any purpose.