Table extraction tools

Marker is an LLM-powered document parser and OCR pipeline designed to convert PDFs and unstructured files into structured markdown, JSON, and HTML. It functions as a data preprocessor that transforms complex documents into machine-readable formats while preserving tables, equations, and layout structures. The system utilizes large language models to refine OCR accuracy, clean mathematical notation, and merge fragmented tables across multiple pages. It employs model-based layout analysis to predict block types and bounding boxes, ensuring a more precise conversion of document elements. Capabi

pdfplumber is a PDF data extraction library and layout analysis tool used to retrieve text, tables, and geometric objects from PDF files using precise coordinate-based analysis. It functions as a layout analyzer and table parser that identifies the bounding boxes and visual coordinates for every character and image on a page. The library distinguishes itself through visual debugging capabilities, allowing users to render PDF pages as images and draw annotations to verify the position of extracted data. It employs line and intersection analysis to identify cell structures and convert unstructu

Camelot is a Python library and processing engine designed to extract tabular data from PDF documents. It converts unstructured tables into machine-readable formats such as CSV, JSON, and Excel. The project provides specialized toolsets for different document types, using line detection for ruled tables and whitespace analysis for borderless tables. It includes an optical character recognition system to recover structured data from image-based scanned PDFs that lack a digital text layer. The library handles complex document layouts, including encrypted files, rotated pages, and tables that s

Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned, machine-readable formats. It functions as a document layout analyzer and a pipeline for extracting structured data and labels using large language models. The system includes a document parsing visualizer, providing a graphical interface to upload documents and inspect the resulting structured data output. The project covers document digitization workflows, including layout analysis to detect headings, tables, and lists, and automated data entry through the cleaning and enri

MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data. It utilizes deep learning models to perform layout analysis, identifying document regions and extracting complex content such as mathematical expressions. By combining these neural network inferences with geometric heuristics, the system reconstructs the reading order and structural hierarchy of documents to ensure accurate data representation. The project distinguishes itself through a multi-stage processing workflow that integrates layout detection, optical character recogn

This project is a PDF data extraction tool and document preprocessor designed to convert PDF files into structured formats such as Markdown, JSON, and HTML. It functions as an OCR document parser for scanned files, an accessibility automator for generating PDF/UA compliant metadata, and a loader for AI orchestration frameworks like LangChain. The software distinguishes itself through specialized handling of complex document elements, including the conversion of mathematical formulas into LaTeX and the generation of natural-language descriptions for charts and images. It utilizes recursive seg

Scrapegraph-ai is a Python framework that uses large language models to automate the extraction of structured data from websites and documents. It functions as an AI-driven data extraction pipeline that converts unstructured web content into structured formats using natural language processing and graph-based logic. The project utilizes graph-based task orchestration to model scraping workflows as interconnected nodes. It features a pluggable model interface for connecting to cloud or local artificial intelligence providers and can generate executable Python code on the fly to handle site-spe

DeepSeek-OCR is a vision processing framework designed to convert image-based text into machine-readable tokens for large language models. It functions as a document inference pipeline that encodes visual data into compact representations, enabling automated optical character recognition and document analysis workflows. The system distinguishes itself through a high-throughput architecture that utilizes hardware-accelerated batch inference to process large volumes of visual data. It incorporates dynamic resolution scaling to manage the balance between visual detail and token consumption, ensu

Docling is a modular framework designed for document parsing, layout analysis, and structured data extraction. It transforms unstructured files and web content into a unified, hierarchical data model that preserves the spatial and semantic relationships between text, tables, images, and layout elements. By normalizing diverse input formats into a consistent internal representation, the library enables uniform processing across various document types. The project distinguishes itself through a schema-driven approach that maps document regions to strongly-typed objects, ensuring data accuracy t

Surya is a document processing platform designed to transform unstructured files into structured, machine-readable data. It provides a comprehensive suite of tools for text recognition, layout analysis, and reading order detection, enabling the conversion of PDFs and images into formats such as JSON, HTML, or markdown. The platform is built to handle complex document workflows, offering capabilities for data extraction, document segmentation, and automated form completion. The platform distinguishes itself through a robust pipeline-based architecture that allows users to chain analysis tasks

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

Camelot is a Python-based library designed to parse, extract, and clean tabular data from PDF files. It converts table elements from text-based PDF documents into programmable data structures and dataframes. The tool identifies tabular regions using coordinate-based grouping, lattice-based line detection, and stream-based text extraction. It can also rasterize PDF pages into images to utilize computer vision for detecting structural lines and boundaries. Extracted data is validated through accuracy and whitespace metrics to filter out low-quality extractions. The processed information can be

Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

PDF-Extract-Kit is a document extraction toolkit designed to convert PDF documents into structured formats such as Markdown, HTML, and LaTeX. It functions as a multi-stage parsing framework that combines a document layout analyzer, a formula recognition engine, an OCR text extractor, and a table extraction system. The project focuses on recovering complex document elements by translating images of mathematical formulas and tabular structures into editable source code. It utilizes model-driven layout analysis to identify structural elements in reports and textbooks while ignoring noise like wa

Pix2Text is an optical character recognition system and document conversion tool designed to transform images and PDFs into Markdown. It functions as a multilingual OCR engine supporting over 80 languages, a LaTeX formula recognizer for mathematical notations, and a parser integrated with vision language models. The project utilizes a hybrid pipeline to separate plain text from mathematical formulas and tabular structures within a single pass. It converts recognized formulas into LaTeX expressions and transforms detected tables and layouts into structured Markdown formatting. The system incl

Zerox is a multimodal document parser and OCR tool that uses vision models to convert PDF files and images into structured Markdown text. It functions as a visual layout extraction engine, leveraging large multimodal models to digitize documents while maintaining their original structural formatting. The system differentiates itself through the use of coordinate-based element mapping and multimodal layout analysis to identify structural elements like tables, charts, and headers. It utilizes rasterization to convert vector PDF pages into high-resolution bitmaps, ensuring consistent input for t

A Repo For Document AI

Tabula is a PDF table extraction tool and data scraper designed to isolate tabular structures within text-based PDF files. It functions as a converter that transforms these layouts into structured CSV or spreadsheet formats for data recovery and analysis. The project provides both a visual interface for manually selecting table areas and a headless command-line interface. This dual approach allows for a choice between manual data recovery via visual-area selection and the integration of table extraction into automated data pipelines. The extraction process utilizes Java-based PDF parsing and

Botasaurus is a Python web scraping framework and headless browser automation system used to build scalable data extraction tools. It functions as a web data extraction tool and OCR document parser, converting website content, images, and PDF files into structured formats such as JSON, CSV, and Excel. The framework distinguishes itself by providing a scraper management interface that allows Python functions to be wrapped in a web-based UI or deployed as standalone desktop applications. This enables non-technical users to trigger extraction jobs and manage tasks via a graphical interface or RE

Marker is a comprehensive document processing platform designed to automate the conversion, extraction, and structuring of data from complex files. It functions as an orchestration engine that chains modular processing steps into versioned, reusable pipelines, allowing organizations to standardize document handling and automate repetitive business tasks at scale. The platform distinguishes itself through its support for secure, private infrastructure deployment, enabling users to run containerized services within their own environments to maintain strict data privacy. It features specialized

This project is an AI-powered document processing engine designed to transform diverse file formats into structured Markdown. By leveraging multimodal language models, it performs complex layout analysis and semantic text extraction, allowing for the conversion of both unstructured files and scanned images into machine-readable content. The toolkit distinguishes itself through a modular, plugin-based architecture that orchestrates multi-stage extraction pipelines. Users can steer the parsing behavior by injecting custom instructions, enabling the system to adapt to domain-specific document st

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

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. The framework integrates layout detection with optical character recognition to convert tabular regions into machine-readable data. It utilizes neural networks to identify and classify structural elements within document images without relying on manual rule-based systems. The system covers a broad range of docu

PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into independent, configurable stages. This architecture supports automated document digitization and multilingual text recognition, capable of identifying text in over one hundred languages across diverse environments ranging from scanned documents to industrial scenes. The framework disti

Extract tables from PDF files

Table Transformer is a deep learning framework designed for document layout analysis and the automated extraction of tabular data from unstructured images and documents. It utilizes a transformer-based architecture to perform object detection, identifying table boundaries and internal grid structures to convert visual information into machine-readable formats. The project distinguishes itself by employing a global optimization strategy for bipartite matching, which eliminates the need for traditional non-maximum suppression during the detection process. By leveraging multi-scale feature extra

This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated know

Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.