Search Results for "text extraction"

Scanopy is a powerful multi-modal data capture and analysis toolkit that enables users to collect, process, and visualize structured and unstructured information from a variety of sources in a flexible pipeline. It is built to handle complex scanning tasks — such as OCR, document analysis, audio transcription, network data capture, and image extraction — while providing unified APIs and workflows that make managing heterogeneous data sources seamless. Developers can compose custom pipelines...

LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking...

The Relationship Visualizer spreadsheet transforms your Excel data into professional Graphviz diagrams and Knowledge Graphs. Enter simple "A is related to B" rows to instantly generate polished relationship graphs using the Graphviz DOT language. Ideal for org charts, dependency graphs, and IT architecture. Free, open-source, MIT-licensed. Customize the look of nodes, edges, and clusters with the Style Designer. Build a CSS-like gallery of reusable styles that lets you apply consistent...

InstaGraph is a Flask application that converts text input or a URL into a visual knowledge graph. It uses an LLM to identify entities, relationships, labels, and node types from the provided content. The generated graph is displayed visually, helping users understand relationships that may be harder to see in plain text. The project includes Graphviz-based visualization logic and supports updating an existing graph through follow-up instructions. It is designed to be simple, colorful, and...

TextGrapher is a Python project that converts unstructured Chinese text into a structured semantic graph. It extracts high-frequency terms, keywords, named entities, and subject-verb-object phrases from an input document. The system then organizes these elements into connected nodes and relationships for visual inspection. Generated results are saved as an HTML graph that can be opened in a browser. The repository includes parsing, keyword extraction, graph construction, and visualization scripts. ...