Turing-Project/WriteGPT
WriteGPT
WriteGPT ist ein End-to-End-System zur Aufsatzautomatisierung, das visuelle Erkennung und automatisierte Textgenerierung kombiniert, um Bilder in fertige digitale Dokumente zu konvertieren. Es fungiert als kreativer Textgenerator und Dokumentenprozessor, der Sprachmodelle nutzt, um langformatige schriftliche Inhalte und Aufsätze zu produzieren.
Das System integriert einen neuronalen Text-Fluency-Evaluator, um die sprachliche Qualität und Natürlichkeit der generierten Prosa zu bewerten. Es enthält zudem einen Transformer-basierten Text-Summarizer, um lange Dokumente in prägnante Zusammenfassungen zu verdichten.
Das Projekt bietet eine Pipeline für optische Zeichenerkennung (OCR), die Text jeder Ausrichtung innerhalb von Bildern erkennt und in maschinenlesbare Zeichen umwandelt. Es unterstützt zudem die automatisierte Dokumentendigitalisierung durch Anordnung generierter Inhalte in strukturellen Layouts, wie Titeln und Absätzen, basierend auf statistischen Schreibmustern.
Features
- Essay Automation Pipelines - Combines visual recognition and automated text generation to convert images into finished digital essays.
- GPT-2 Implementations - Implements the GPT-2 architecture to produce long-form written content and essays.
- Long-Form Text Generation - Produces long-form creative content and essays using language models trained on high-quality prose.
- Optical Character Recognition - Implements neural networks for converting images of text into machine-encoded characters.
- Multilingual Text Recognition - Converts image-based text into machine-readable characters using neural networks trained on multilingual corpora.
- Transformer Language Models - Employs a transformer-based language model to predict tokens for producing high-quality prose.
- Document Digitization Tools - Turns physical papers into structured digital documents by combining text recognition and layout formatting.
- Automated Essay Generators - Combines visual recognition and automated text generation to convert images into finished essays.
- OCR Document Processors - Provides a pipeline that converts image-based text into machine-readable characters using neural networks.
- Text Orientation Mapping - Generates precise geometric shapes and coordinates to locate text of any orientation within images.
- Text Detectors in Images - Locates text regions of any orientation within images using deep learning.
- Quality Evaluators - Evaluates the linguistic fluency and naturalness of generated content via neural scoring networks.
- Text - Uses a scoring network to assess the linguistic quality and naturalness of generated writing.
- Text Summarization - Uses transformer-based encoding to condense long-form documents into concise summaries.
- Linguistic Probability Scoring - Calculates the statistical probability of word sequences to evaluate the naturalness of generated text.
- Text Summarization - Uses transformer encoding to condense long-form text into concise summaries.
- Structural Layout Generation - Arranges generated content into titles and paragraphs based on statistical writing patterns.
- Document Layout and Styling - Organizes generated text into structural layouts featuring titles and paragraphs.
- Naturalness Scoring - Implements a scoring network to evaluate the linguistic naturalness and structural quality of generated prose.
