robertknight/ocrs
Ocrs
This project is a terminal-based optical character recognition engine that uses neural network models to extract text and spatial layout data from images. It functions as both a command-line utility for automated text processing and a library for integrating machine learning-powered recognition into broader workflows.
The engine distinguishes itself through a modular processing pipeline that supports custom model loading and memory-mapped weight initialization for efficient execution. It preserves document structure by tracking precise geometric coordinates for every detected text element, and it allows for output refinement through character-level validation rules.
The system includes comprehensive tools for image ingestion, including direct capture from system clipboards and browser content. It provides diagnostic capabilities by generating visual overlays and intermediate processing artifacts to verify recognition accuracy and troubleshoot pipeline performance.
The software is distributed as a static binary to ensure portability across environments without requiring external dependencies.
Features
- OCR Command Line Interfaces - Provides a terminal-based utility for processing images and clipboard data into structured text with spatial coordinates.
- Document Spatial Coordinate Outputs - Returns extracted text with precise spatial coordinates to preserve document structure.
- Image Text Extractions - Processes raw pixel data to identify and return text content as strings or coordinate-mapped lines.
- Neural Network Model Implementations - Executes pre-trained neural network models to perform pattern recognition on raw pixel data.
- Visual Text Extractors - Identifies and digitizes text regions from visual inputs using machine learning pipelines.
- Optical Character Recognition - Provides a library for extracting text and spatial layout data from images and clipboard content.
- Memory-Mapped Weight Loaders - Maps model weight files directly into process memory to reduce RAM usage and improve initialization speed.
- Model Initialization Loaders - Loads neural network models from file paths or memory buffers to prepare the engine for analysis.
- Document Text Recognition Toolkits - Supports loading custom neural network models to perform specialized text recognition tasks.
- Output Formats - Exports extracted text and precise bounding box coordinates into structured data files for downstream processing.
- Standard Input Ingestion - Ingests image data from standard input streams or clipboard buffers for automated processing workflows.
- Clipboard Image Pasting - Captures and processes image data directly from the system clipboard for rapid text extraction.
- Layout Preservation - Exports extracted text and spatial coordinates to preserve the original document structure.
- Clipboard Data Extraction - Extracts text content from the system clipboard to convert visual information into editable text.
- Static Binary Compilers - Compiles the entire engine into a single static binary for portable, dependency-free execution.
- Image Processing Pipelines - Chains modular image processing stages like binarization and feature extraction before final recognition.
- OCR Character Set Constraints - Restricts recognition output to specific character sets to improve accuracy on constrained inputs.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
