tesseract-ocr/tessdata

Features

• Text Recognition - Provides a comprehensive collection of trained models for converting detected text images into machine-readable characters.
• Optical Character Recognition - Digitizes printed characters from image files using trained language models.
• Embedded Text Recognizers - Provides the essential trained models for extracting and interpreting text embedded within images across global scripts.
• Multilingual Models - Provides pre-trained models that enable the extraction of written text across various global languages.
• Multilingual Text Recognition - Extracts text from images using trained model files for a wide variety of global scripts and languages.
• Multi-Language Recognition Models - Provides language-specific recognition models to identify and extract text from images.
• OCR Language Datasets - Provides trained models and language-specific data files to enable text extraction from images.
• OCR Training Datasets - Provides language-specific datasets and linguistic patterns used to improve OCR accuracy across global languages.
• OCR Language Training - Defines characters and linguistic patterns via trained data files for specific language recognition.
• Pre-trained OCR Language Data - Provides the pre-trained neural network and legacy data files used by Tesseract to recognize and extract text from images.
• Multilingual Text Processing - Ships specialized language data files used for identifying and extracting text across multiple global scripts.
• Optical Character Recognition - Provides the pre-trained neural network and legacy data files that enable the conversion of images of text into encoded text.
• Tesseract Model Data - Provides the necessary trained LSTM and legacy model files that enable Tesseract OCR engines to function.
• Long Short-Term Memory Networks - Uses Long Short-Term Memory networks to predict character sequences from image slices.
• LSTM-Based OCR Models - Ships a collection of Long Short-Term Memory trained data for high-accuracy optical character recognition.
• Text and Language Models - Supplies trained neural network files for identifying text in specific languages and scripts.
• Optical Character Recognitions - Provides language-specific data files for optical character recognition of printed text.
• Model Binary Formats - Provides compiled binary files containing language-specific weights and neural network parameters.
• Document Layout Analysis - Analyzes image layouts to determine text rotation and script direction.
• Pattern-Matching Recognition - Provides non-neural pattern-matching models as a fallback for specific font styles.
• Korean Text Recognition - Identifies and extracts Korean characters from images using trained linguistic models.
• Japanese Text Recognition - Extracts printed Japanese characters from images using trained language model data.
• Model Pruning - Implements model pruning to reduce the size and computational requirements of neural network weights.
• Script and Orientation Detectors - Includes specialized models to identify the writing system and rotation angle of text within images.
• Document Digitization Tools - Provides the linguistic and visual data necessary for converting physical document scans into searchable digital formats.
• Text Orientation Detection - Ships trained data to detect and correct document text orientation and script direction.
• Language Data Partitioning - Separates linguistic patterns and character sets into discrete files for optimized loading of required scripts.

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