Umi-OCR: Free OCR Text Recognition Tool

What is Umi-OCR?

Umi-OCR is a free, open-source, offline OCR text recognition software. It works without an internet connection and is ready to use upon extraction. It supports text recognition from screenshots, batch images, and PDF scans, and can recognize mathematical formulas and QR codes. It can generate double-layer searchable PDFs. The software includes a multi-language recognition library, supports multi-language interface switching, and provides command-line and HTTP interface call functions. Its plugin-based design allows for the extension of more features, such as importing different language recognition libraries.

Main Features of Umi-OCR

• Screenshot OCR: Quickly recognizes text in screenshots, supports layout parsing, and outputs text content in the correct order.
• Batch Image OCR: Supports batch recognition of text in images, with the ability to set ignore areas to exclude watermarks and other interference.
• PDF Recognition and Processing: Extracts text from PDF scans and converts PDFs into double-layer searchable PDFs for easier editing and searching.
• QR Code Recognition and Generation: Scans QR codes to retrieve information and also generates QR code images.
• Formula Recognition: Recognizes mathematical formulas, helping users quickly extract and edit formula content.
• Multi-language Support: Includes a multi-language recognition library and supports text recognition in multiple languages, with a multi-language interface.
• Flexible Invocation: Supports external invocation methods such as command-line and HTTP interfaces, making it easy to integrate with other software or tools.

Technical Principles of Umi-OCR

• Image Preprocessing: Performs grayscale, binarization, and noise reduction on input images to enhance text clarity and reduce background interference, providing clearer images for subsequent text detection and recognition.
• Text Detection: Uses algorithms like Convolutional Neural Networks (CNN) to detect text regions in images and segment them. It can recognize text regions with different fonts, sizes, and arrangements.
• Text Recognition: Extracts features from detected text regions and uses deep learning models (such as PaddleOCR-based models) for classification recognition, converting text images into computer-readable text information.
• Post-processing: Corrects and formats the recognition results, such as merging text from the same paragraph and processing vertical text, optimizing 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, etc., into editable electronic text, improving document storage and retrieval efficiency.
• Automated Data Entry: In enterprises, Umi-OCR can be used to automatically extract data from invoices, reports, certificates, etc., reducing manual input errors and improving work efficiency.
• Education Sector: Teachers can use Umi-OCR to convert text from textbooks or exam papers into text format, making it easier for students to read and answer questions.
• Software Interface Text Extraction: Suitable for extracting text from software interfaces where text cannot be copied, such as games and image editors.
• Machine Learning Data Preprocessing: In Natural Language Processing (NLP) tasks, Umi-OCR can convert scanned text into training data.