hiroi-sora/Umi-OCR
Umi OCR
Umi-OCR is an optical character recognition engine designed to convert visual text from images and documents into machine-readable character data. It functions as a local-first toolkit, processing all visual data directly on the host machine using embedded neural network models to maintain privacy and offline availability.
The project distinguishes itself through its focus on automated document digitization and integrated barcode and QR code decoding. By utilizing a modular, Python-based orchestration layer, it enables users to transform static image files and multi-page documents into searchable text formats. The system is built to handle high-volume tasks, employing asynchronous task queueing to maintain throughput during batch processing operations.
Beyond its core recognition capabilities, the software provides a command-line interface that allows for the automation of repetitive extraction workflows. This interface exposes internal processing functions to external scripts, enabling the execution of batch recognition tasks without manual intervention. The project maintains consistent functionality across different operating system environments through its cross-platform native integration.
Features
- Optical Character Recognition - Performs optical character recognition on image files to extract text and associated metadata.
- Local Inference Engines - Processes visual data entirely on the host machine using embedded neural network models for privacy and offline use.
- Document Analysis Tools - Converts document pages into readable text by analyzing page layouts and returning identified character strings.
- Barcode Decoders - Extracts hidden text from image files by identifying and decoding visual patterns found within scanned codes.
