Search Results for "structured text"
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Showing 153 open source projects for "structured text"
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Recognizers-Text
Recognition and resolution of numbers, units, date/time, etc.
Recognizers-Text is a multilingual text recognition library that extracts structured information such as dates, numbers, and currency values from unstructured text. -
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text-extract-api
Document (PDF, Word, PPTX ...) extraction and parse API
text-extract-api is an open-source service designed to extract readable text from a wide variety of document formats through a simple API interface. The project focuses on converting complex files such as PDFs, images, scanned documents, and office files into structured plain text that can be processed by downstream applications or language models. -
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LiteParse
A fast, helpful, and open-source document parser
LiteParse is an open-source lightweight parsing library designed to extract structured data from unstructured text using large language models in an efficient and cost-effective manner. It focuses on simplifying the process of turning raw text into structured outputs such as JSON by providing a streamlined interface for prompt-based parsing. The system is designed to minimize overhead, making it suitable for applications where performance and cost are critical considerations. ... -
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PaddleOCR-json
OCR offline image text recognition command line windows program
PaddleOCR-json is an OCR engine based on the PaddleOCR project that provides a command-line interface and tools for extracting text from images and exporting results in structured JSON format. It wraps the PaddleOCR models, which are capable of detecting and recognizing text in a wide variety of languages and layouts, into a self-contained executable that can be run locally without needing a deep learning environment configured manually. This makes it practical for developers or system integrators who want reliable OCR output in JSON while avoiding the complexity of training or managing models by hand. ... -
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LlamaParse
Parse files for optimal RAG
LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers. -
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MarkPDFDown
A high-quality PDF to Markdown tool based on large language model
...By producing Markdown rather than raw text, the tool makes it easier to integrate documents into knowledge bases, documentation systems, or language model pipelines that rely on structured input. The software is particularly useful for developers working with technical documents, academic papers, or reports that need to be indexed, summarized, or processed by downstream AI systems. -
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Ideogram 4
Open image model at the forefront of design
Ideogram 4 is an open-weight text-to-image model focused on high-quality visual generation, design control, and accurate text rendering inside images. It is built for users who need more than generic image generation, especially when layout, typography, composition, color, and language understanding matter. The project introduces a structured JSON prompting workflow that gives creators more explicit control over scene details and visual constraints. -
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DeepSeek-OCR
Contexts Optical Compression
DeepSeek-OCR is an open-source optical character recognition solution built as part of the broader DeepSeek AI vision-language ecosystem. It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body... -
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Humanizer Skill
Claude Code skill that removes signs of AI-generated writing from text
Humanizer Skill is a utility library focused on transforming technical or machine-oriented text into expressions that are more natural, readable, and “human-friendly.” It provides a suite of algorithms that convert timestamps, identifiers, file sizes, code tokens, and structured data into phrases that resemble typical human phrasing rather than compact machine output. For example, date and time values can be expressed as relative terms (“two hours ago”), and file sizes can be shown in intuitive units instead of raw bytes. ... -
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
ai-notes is a curated repository of notes focused on the evolving landscape of artificial intelligence, particularly generative models and large language models. It is designed to help software engineers quickly understand modern AI concepts, tools, and developments through structured documentation and research notes. It functions as a living knowledge base composed of numerous markdown files that organize topics such as text generation, image generation, AI infrastructure, and code generation models. These notes include observations, references, experiments, and summaries of important research and industry developments in AI. ai-notes also contains collections of prompts, curated learning materials, and categorized resources intended to help developers explore AI capabilities and practical applications. -
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BlogWizard
Generate blog articles from video or audio
BlogWizard is a demo/utility project built on top of Groq’s LLM infrastructure that converts video or audio content into well-structured blog posts, enabling creators to repurpose multimedia content into text — useful for SEO, accessibility, or reaching audiences that prefer reading. The tool uses transcription (e.g. via Whisper) to extract text from audio/video, then runs an LLM-based generation pipeline to transform that content into coherent, readable blog-format posts — with sections, formatting, and possibly metadata. ... -
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LLM Scraper
Extract structured data from webpages using LLM-powered scraping
LLM Scraper is a TypeScript library designed to extract structured data from webpages using large language models. Instead of relying on fragile HTML selectors or manual parsing rules, the tool interprets webpage content with language models and converts it into structured data according to a defined schema. Developers can specify the data structure using tools such as Zod or JSON Schema, enabling the model to extract relevant information directly into typed objects. -
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kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing higher-quality graph structures from raw text. -
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Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to... -
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Chandra
OCR model for complex documents with layout-aware structured outputs
Chandra is an advanced OCR model designed to extract and structure information from complex documents such as tables, forms, handwritten notes, and mathematical content. It focuses on preserving full document layout, meaning that extracted text is accompanied by positional metadata like bounding boxes for each element. Chandra supports multiple output formats including Markdown, HTML, and JSON, making it suitable for downstream processing and integration into data pipelines. It is capable of... -
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BrowserAI
Run local LLMs like llama, deepseek, kokoro etc. inside your browser
...The platform provides a developer-friendly SDK with pre-configured popular models, and it allows for seamless switching between MLC and Transformer engines. Additionally, it supports features such as speech recognition, text-to-speech, structured output generation, and Web Worker support for non-blocking UI performance. -
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Audiblez
Generate audiobooks from e-books
Audiblez is a tool for generating high-quality .m4b audiobooks directly from .epub e-books using the Kokoro-82M neural text-to-speech model. It focuses on making audiobook creation easy and fast: from a single command, the tool splits an e-book into chapters, synthesizes audio for each section, and then merges the results into a structured audiobook with chapter-based WAV files and a final .m4b container. The Kokoro-82M model it uses is compact (82M parameters) yet natural sounding, trained on under 100 hours of audio, and supports multiple languages, including English (US/UK), Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese, and Mandarin Chinese. ... -
18
HeartMuLa
A Family of Open Sourced Music Foundation Models
...The project also includes HeartCodec, a music codec optimized for high reconstruction fidelity, enabling efficient tokenization and reconstruction workflows that are critical for training and generation pipelines. For text extraction from audio, it provides HeartTranscriptor, a Whisper-based model tuned specifically for lyrics transcription, which helps bridge generated or recorded audio back into structured text. It also introduces HeartCLAP, which aligns audio and text into a shared embedding space. -
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node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama
...The system automatically detects the available hardware on a machine and selects the most appropriate compute backend, including CPU or GPU acceleration. Developers can use the library to perform tasks such as text generation, conversational chat, embedding generation, and structured output generation. Because it runs models locally, the platform is particularly useful for privacy-sensitive environments or offline AI deployments. -
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BAML
The AI framework that adds the engineering to prompt engineering
BAML is an open-source framework and domain-specific language designed to bring structured engineering practices to prompt development for large language model applications. Instead of treating prompts as unstructured text, BAML introduces a schema-driven approach where prompts are defined as typed functions with explicit inputs and outputs. This design allows developers to treat language model interactions as predictable software components rather than ad-hoc prompt strings. ... -
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Search-Index
A persistent, network resilient, full text search library
Search-Index is a lightweight and fast JavaScript-based search engine that enables full-text search indexing and retrieval for web applications. -
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NarratoAI
Using AI models to automatically provide commentary and edit videos
NarratoAI is an open-source platform designed to automate the generation of narrative content using artificial intelligence. The system combines large language models with media processing capabilities to create scripts, stories, and structured narrative outputs from user inputs. NarratoAI supports workflows where users provide prompts, themes, or source materials, and the software organizes them into coherent narrative structures suitable for articles, scripts, or multimedia storytelling. The project integrates multiple AI components such as text generation models, content structuring pipelines, and automated editing tools to streamline content creation. ... -
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Hiring Agent
AI agent to evaluate and score resumes
Hiring Agent is an AI-powered resume evaluation pipeline for screening technical candidates. It reads a resume PDF and converts the content into Markdown-like text. It then uses a local or hosted language model to extract structured candidate information into sectioned JSON. The system can enrich that resume data with GitHub profile and repository signals when a profile is available. After the data is collected, it produces an explainable evaluation with category scores, supporting evidence, bonus points, and deductions. ... -
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NeMo Retriever Library
Document content and metadata extraction microservice
NeMo Retriever Library is a scalable microservice framework designed for extracting, structuring, and enriching content from documents to support downstream generative AI applications. It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient handling of large datasets. It supports multiple extraction strategies for different document formats, balancing accuracy and throughput depending on the use case. ... -
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repo2txt
Web-based tool converts GitHub repository contents
repo2txt is an open-source developer tool that converts the contents of a code repository into a single structured text file that can be easily consumed by large language models. The tool is designed to address the challenge of analyzing entire codebases with AI assistants, where code is normally distributed across many files and directories. By collecting repository contents and formatting them into a single text document, repo2txt allows developers to feed complete projects into AI systems for analysis, documentation, or code explanation tasks. ...
