What does layout-parser/layout-parser do?

Layout-parser is a deep learning document layout parser and image analysis framework. It provides a toolkit for extracting structural information and layout patterns from scanned documents and digital images, transforming them into programmatic data structures for automated analysis.

What are the main features of layout-parser/layout-parser?

The main features of layout-parser/layout-parser are: Document Layout Analysis, Layout Parsing Toolkits, Document Analysis Models, Document Structure Analysis, Hierarchical Representations, Tabular Grid Mapping, Document Region Detectors, Table Structure Reconstructions.

What are some open-source alternatives to layout-parser/layout-parser?

Open-source alternatives to layout-parser/layout-parser include: kreuzberg-dev/kreuzberg — Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into… pymupdf/pymupdf — PyMuPDF is a comprehensive PDF manipulation library and document analysis tool. It serves as a text extraction tool,… grobidorg/grobid — Grobid is a machine learning system designed to transform academic and scientific PDF publications into structured… oomol-lab/pdf-craft — pdf-craft is an OCR-based document parser and structure extractor designed to convert PDF files into structured data,… funstory-ai/babeldoc — BabelDOC is a technical document translation system designed to translate PDF files while preserving their original… bytedance/dolphin — Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital…

Layout Parser - डॉक्यूमेंट लेआउट स्ट्रक्चर एक…

Layout Parser - डॉक्यूमेंट लेआउट स्ट्रक्चर एक… | Awesome Repos

Features

Document Layout Analysis - Provides deep learning tools to identify complex structures and layout elements from document images.
Layout Parsing Toolkits - Provides a deep learning toolkit to detect and analyze structural elements within document images.
Document Analysis Models - Applies neural networks to detect and classify document components without relying on manual rules.
Document Structure Analysis - Extracts layout and text from images into specialized programmatic data structures for analysis.
Hierarchical Representations - Organizes detected document elements into a parent-child tree structure to preserve logical information flow.
Tabular Grid Mapping - Combines visual region detection with OCR to map textual content into structured tabular grids.
Document Region Detectors - Uses deep learning neural networks to identify and classify structural regions within document images.
Table Structure Reconstructions - Transforms tabular regions into machine-readable formats by reconstructing logical grids from visual elements.
Visual Structural Elements - Locates and identifies specific structural elements within document images using deep learning models.
Document Extraction Tools - Offers a library for parsing document images into programmatic data structures for downstream analysis.
Tabular Data Extraction - Converts tabular data from document images into machine-readable formats using layout detection and OCR.
Pixel Coordinate Mappings - Maps neural network bounding box outputs to normalized pixel coordinates for consistent document analysis.
Bounding Box Visualizers - Renders detection masks and bounding boxes over original images for manual verification of parsing accuracy.
OCR Layout Integration Pipelines - Combines layout detection with OCR in a pipeline to convert tabular regions into machine-readable data.
Parsing Verification Overlays - Renders detected layout elements and hierarchies visually to verify automated document parsing accuracy.
Layout Visualization Tools - Renders detected document elements and hierarchies visually to verify automated parsing accuracy.
Unified Model Wrappers - Standardizes different deep learning backends under a single API to allow swapping detection models seamlessly.
PDF Processing Tools - Deep learning-based tool for document layout analysis.

Layout Parser के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Layout Parser के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

kreuzberg-dev/kreuzberg
kreuzberg-dev/kreuzberg
8,527GitHub पर देखें
Kreuzberg 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
Rustdocument-intelligenceelixirffi
GitHub पर देखें8,527
grobidorg/grobid
grobidOrg/grobid
4,954GitHub पर देखें
Grobid is a machine learning system designed to transform academic and scientific PDF publications into structured XML. It functions as a PDF to XML parser and scholarly metadata extractor, identifying and normalizing titles, authors, affiliations, and bibliographic references from research papers. The system utilizes a deep learning document segmenter to divide raw PDFs into functional regions and employs a bibliographic reference resolver to match citations against external registries for metadata enrichment and DOI resolution. It supports a full machine learning model training pipeline, al
Javabibliographical-referencescrfdeep-learning
GitHub पर देखें4,954
funstory-ai/babeldoc
funstory-ai/BabelDOC
7,752GitHub पर देखें
BabelDOC is a technical document translation system designed to translate PDF files while preserving their original layout and styling. It functions as a layout-preserving translator that utilizes large language models to convert content into target languages, specifically tailored for scientific and technical documents. The system distinguishes itself through specialized handling of academic content, including the identification and preservation of mathematical formulas and complex layout structures. It ensures technical accuracy by employing glossary-driven terminology enforcement, using so
Python
GitHub पर देखें7,752
oomol-lab/pdf-craft
oomol-lab/pdf-craft
4,867GitHub पर देखें
pdf-craft is an OCR-based document parser and structure extractor designed to convert PDF files into structured data, Markdown, or EPUB ebooks. It utilizes optical character recognition and statistical analysis to identify document hierarchies and extract text and structured content. The system features specialized rendering for mathematical formulas and tables, using heuristic reconstruction to convert tabular data into digital formats. It includes a document structure extractor that builds tables of contents by analyzing font sizes, linguistic patterns, and language model title detection.
Pythondeepseek-ocrdocumentocr
GitHub पर देखें4,867

Layout Parser के सभी 30 विकल्प देखें

Layout-Parserlayout-parser

Layout Parser

Features

Layout Parser के ओपन-सोर्स विकल्प

kreuzberg-dev/kreuzberg

grobidOrg/grobid

funstory-ai/BabelDOC

oomol-lab/pdf-craft

Frequently asked questions

स्टार हिस्ट्री

Frequently asked questions

Layout Parser के ओपन-सोर्स विकल्प

kreuzberg-dev/kreuzberg

grobidOrg/grobid

funstory-ai/BabelDOC

oomol-lab/pdf-craft