VietOCR

VietOCR is an optical character recognition application designed to process scanned images containing text in the Vietnamese language. The software converts scanned documents in image format into editable text. The tool works with image files and PDF documents. The recognition process is based on the Tesseract OCR framework.

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• Vietnamese text recognition. The main function of the software is the recognition of Vietnamese characters with high accuracy. The OCR engine is specifically trained to identify diacritics and special characters of the Vietnamese alphabet. The system handles different font styles and sizes in scanned documents.
• Batch processing. The ability to process multiple image files simultaneously streamlines work with large volumes of documents. Users can select multiple files or entire folders for conversion. Automatic sequential processing eliminates the need for manual intervention for each document.
• PDF format support. This feature allows extracting text from scanned PDF documents. The software can process both single-page PDFs and multi-page documents. The recognition results are integrated into a single output file that maintains the structure of the original document.
• Spell checking. The integrated spell checker identifies and flags words that do not match the Vietnamese dictionary. Users can review and correct recognition errors through contextual suggestions. The dictionary contains contemporary vocabulary and common technical terms.
• Image preprocessing. Image enhancement tools optimize the quality of scanned documents before recognition. Available filters include contrast adjustment, noise removal, and skew correction. These operations improve recognition accuracy on poor-quality documents.
• Graphical user interface. The application provides a visual interface for all OCR operations. Users can drag and drop files directly into the application window. The layout of controls follows established patterns in productivity applications.
• Multi-language recognition. In addition to Vietnamese, the software recognizes text in English, French, German, and other languages. Users can select the recognition language based on the document's content. Multi-language configuration allows processing multilingual documents.
• Export to editable formats. Recognition results are saved in plain text, RTF, or Microsoft Word formats. Format preservation includes line breaks, paragraphs, and basic document structure. Exported files are compatible with standard word processors.
• Integration with Tesseract OCR. The application uses the Tesseract OCR engine as the technological foundation for character recognition. The integration includes updated versions of the engine with improvements in accuracy and speed. Users can adjust specific engine parameters for specialized use cases.
• Scanned document processing. The feature handles scanned documents with different resolutions and lighting conditions. The algorithm compensates for common distortions such as shadows, stains, and aging marks. Adaptive recognition adjusts to variations in original print quality.
• OCR parameter configuration. Advanced users can modify recognition engine settings to optimize results. Available adjustments include confidence thresholds, page segmentation modes, and preprocessing methods. Customization allows adapting the software to specific document types.
• Table and column recognition. The feature identifies and preserves the tabular structure in scanned documents. The algorithm automatically detects column layouts and table borders. The recognized text maintains the visual organization of the original document.

The development history of VietOCR began in 2009. The project's creator is Quan Nguyen. The software is developed as a Java application that provides a graphical interface for the Tesseract OCR engine. The choice of Java enables the software's cross-platform capability. Initial versions focused on basic Vietnamese text recognition. Subsequent updates incorporated advanced image processing and spell checking features.

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