2 dépôts
Awesome GitHub RepositoriesTree Representations
Represents a document as a flat array of nodes with index-based parent/child references forming a tree.
Distinct from Structured Document Extraction: Distinct from Structured Document Extraction: focuses on the internal tree representation of the document structure rather than the conversion of visual layouts to machine-readable formats.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Tree Representations. Refine with filters or upvote what's useful.
Awesome Tree Representations GitHub Repositories
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- kreuzberg-dev/kreuzberg
kreuzberg-dev/kreuzberg
8,527Voir sur GitHubKreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo
Provides a traversable tree of document nodes with parent-child references for knowledge graph construction.
Rustdocument-intelligenceelixirffi - mindee/doctr
mindee/doctr
6,149Voir sur GitHubDocTR is a deep learning OCR library built on PyTorch that detects and transcribes text in document images using a two-stage detection-recognition pipeline. It provides a complete framework for building and deploying OCR pipelines with pretrained models available through the Hugging Face Hub, and supports exporting trained models to ONNX format for cross-runtime deployment. The library offers end-to-end OCR pipelines that combine text detection and recognition to extract all text from document images or PDFs, with support for rotated page handling and varied text orientations. It includes cap
Structures detected text into a hierarchy of words, lines, blocks, pages, and documents.
Pythondeep-learningdocument-recognitionocr
