What is Umi-OCR
Umi-OCR is a free and open-source offline OCR text recognition software. No internet connection is required, and it is ready to use after extraction. It supports text recognition for screenshots, batch images, and PDF scans, can recognize mathematical formulas and QR codes, and can generate double-layer searchable PDFs. It has built-in multilingual recognition libraries, the interface supports multilingual switching, and it provides command-line and HTTP API calling capabilities. Its plugin-based design can extend more functions, such as importing recognition libraries for different languages.
Main features of Umi-OCR
- Screenshot OCR: Can quickly recognize text in screenshots, supports layout parsing, and outputs text content in the correct order.
- Batch image OCR: Supports batch recognition of text in images, and you can set ignored areas to exclude interfering content such as screenshot watermarks.
- PDF recognition and processing: Can extract text from PDF scans and convert PDFs into double-layer searchable PDFs, making subsequent editing and searching easier.
- QR code recognition and generation: Supports scanning QR codes to obtain information, and also supports generating QR code images.
- Formula recognition: Has mathematical formula recognition capabilities, helping users quickly extract and edit formula content.
- Multilingual support: Has built-in recognition libraries for multiple languages, supports text recognition in many languages, and the interface also supports multilingual switching.
- Flexible invocation: Supports external invocation methods such as command line and HTTP API, making it easy to integrate with other software or tools.
Technical principles of Umi-OCR
- Image preprocessing: Performs grayscale conversion, binarization, denoising, and other operations on input images to improve text clarity, reduce background interference, and provide clearer images for subsequent text detection and recognition.
- Text detection: Uses algorithms such as convolutional neural networks (CNN) to detect text areas in images and segment them out. It can recognize text areas with different fonts, sizes, and arrangements.
- Text recognition: Performs feature extraction on detected text areas and uses deep learning models (such as models based on PaddleOCR) for classification and recognition, converting text images into computer-readable text information.
- Post-processing: Performs correction, layout, and other operations on recognition results, such as merging text in the same natural paragraph and handling vertical text, to optimize the final output format.
Project address of Umi-OCR
- Github repository：https://github.com/hiroi-sora/Umi-OCR
Application scenarios of Umi-OCR
- Document digitization: Umi-OCR can convert paper documents, books, contracts, and more into editable electronic text, improving the efficiency of document storage and retrieval.
- Automated data entry: In enterprises, Umi-OCR can be used to automatically extract data from documents such as invoices, reports, and certificates, reducing manual input errors and improving work efficiency.
- Education field: Teachers can use Umi-OCR to convert image text in textbooks or exam papers into text format, making it convenient for students to read and answer questions.
- Text extraction from software interfaces: Suitable for software where text cannot be copied, such as text extraction in games and image editors.
- Machine learning data preprocessing: In natural language processing (NLP) tasks, Umi-OCR can convert scanned text into training data.