What is Optical Character Recognition?
Optical Character Recognition (OCR) is a foundational computer vision technology that converts printed or handwritten text from images or scanned documents into machine-readable digital text. Traditional OCR systems analyze the shape, position, and pattern of characters in an image, mapping them against a pre-trained character model to extract structured text.
OCR has become critical in transforming analog documents into searchable and editable formats, driving use cases such as:
- Document digitization
- Automated data entry
- Accessibility enhancements (e.g., text-to-speech for visually impaired users)
Recent advances, particularly in machine learning and deep neural networks, have significantly improved OCR’s accuracy across diverse domains and languages.
OCR for Code at Pieces
At Pieces, we’ve extended OCR’s capabilities beyond traditional document processing by tailoring it to recognize and accurately transcribe programming code from images. This adaptation is critical, as source code demands not only character-level accuracy but also preservation of layout and syntactic structure.
OCR Engine Choice: Tesseract + LSTM
We selected Tesseract—an open-source OCR engine—as our base. Tesseract supports over 100 languages and integrates LSTM-based sequence prediction, offering a solid starting point for structured text recognition. However, out-of-the-box, Tesseract is not optimized for code syntax or indentation.
To address this, we developed a specialized OCR pipeline with pre-processing, post-processing, and layout inference tailored to the needs of developers.
Image Pre-Processing for Code Screenshots
To optimize OCR for code, we standardized inputs through a robust image pre-processing pipeline, particularly for images captured from:
- IDEs (e.g., VS Code, IntelliJ)
- Terminals and command lines
- Code screenshots from YouTube tutorials or blog posts
Key Challenges & Solutions
1. Dark Mode and Color Inversion
Tesseract performs best on binarized, light-background images. We implemented an automatic dark-mode detection pipeline:
- Median blur to reduce visual outliers
- Pixel brightness thresholding to classify image mode
- Inversion applied conditionally for dark backgrounds
2. Noisy or Gradient Backgrounds
We apply a dilation + median blur technique:
- A duplicate image is blurred and dilated
- Subtracting the blurred image from the original removes background noise while preserving text edges
3. Low-Resolution Images
Using bicubic upsampling, we scale images to improve OCR performance. Although we evaluated SRCNN (Super-Resolution CNN) and found it comparable in accuracy, its computational overhead and storage requirements led us to favor bicubic for production use.
Post-OCR: Code Layout and Indentation Inference
OCR for code requires structure preservation—particularly indentation, which is semantically critical in languages like Python.
Layout Inference Strategy:
- We leverage Tesseract’s bounding boxes per line
- By computing average character width per box and comparing starting X-coordinates, we infer relative indentation
- A heuristic is applied to normalize indent levels to even-space units (e.g., 2 or 4 spaces)
This enables rendering of clean, readable, and semantically valid source code from OCR output.
Evaluation Methodology
We evaluate each modification in our pipeline through empirical validation using handcrafted and synthetic datasets of code-image pairs.
Evaluation Metrics:
- Levenshtein Distance: Measures edit distance between OCR output and ground truth
- Hypothesis-driven testing: Each enhancement (e.g., upsampling method, noise removal) is treated as a hypothesis, validated through A/B testing across datasets
For example:
Hypothesis: SRCNN will outperform bicubic interpolation for low-res code images
Result: Bicubic delivered comparable accuracy with lower resource overhead, and was chosen for production
Summary: Tailoring OCR for Code is Non-Trivial
Standard OCR engines are not code-aware. They:
- Ignore indentation
- Struggle with noisy UIs
- Lack syntax sensitivity
Our enhancements—preprocessing, layout-aware postprocessing, and tailored evaluation—enable production-grade OCR for developers, delivering usable, syntactically correct code from screenshots and video frames.
Get Started with Pieces OCR
You can experience our OCR model by downloading the Pieces desktop app, built for seamless code extraction from images.
We’re also expanding our developer tooling ecosystem:
- Integrations MCP with GitHub and Cursor
- Recent implementation of MCP workflows
Interested in our APIs? Contact: email me.
Related Technical Articles
- Text Segmentation in Retrieval-Augmented Generation (RAG)
- Converting Dart Chrome
- Context Management for Repository-Aware Code Generation
- Fast Entity Resolution in Dataflows
Our Documentation: https://docs.pieces.app/products/meet-pieces
Top comments (1)
Thank you for addressing such a niche and often overlooked topic! It’s great to see the unique challenges of OCR for code being tackled in detail. Your insights into layout and indentation handling were especially enlightening.