Engineering Drawing OCR

Sanitized technical case study for OCR work on engineering drawings and title-block text.

Context

This repository documents part of a private project focused on text recognition in technical drawings. The public version is intentionally limited: it shows architecture, preprocessing ideas, model structure, and evaluation workflow without publishing private drawings, labeled crops, model weights, or full application code.

What It Shows

• image preprocessing for scanned drawings;
• deskew and noise reduction before OCR;
• region-of-interest handling for title-block style areas;
• integration approach for switching between an OCR engine and a custom recognizer;
• CRNN + CTC model structure for narrow technical text lines;
• synthetic-data and real-crop training loop design;
• evaluation notes for character error rate and manual verification.

Public Scope

Included:

• reference preprocessing code;
• reference CRNN model structure;
• architecture notes;
• training and evaluation notes;
• privacy and publication boundaries.

Not included:

• private source repository;
• real engineering drawings or PDFs;
• labeled production crops;
• model checkpoints;
• full GUI / database application code;
• client-specific data, paths, logs, or business context.

Repository Structure

assets/
  drawing-ocr-pipeline.svg
docs/
  01-architecture.md
  02-training-loop.md
  03-publication-boundaries.md
src/
  preprocessing_reference.py
  crnn_reference.py

Status

Public limited-code case study. The original work included a broader local application and experimental model training pipeline; this repository keeps only the parts that are safe and useful to show publicly.