pdf2docx is a suite of PDF utilities designed to transform static PDF documents into editable DOCX files. It functions as a multi-core processor capable of accelerating the conversion of large files by distributing page tasks across multiple CPU cores.
الميزات الرئيسية لـ artifexsoftware/pdf2docx هي: PDF to DOCX Converters, Parallel Processing, Table Extraction Utilities, Tabular Data Reconstruction, Document Processing Parallelization, Tabular Data Extraction, Decryption Utilities, Layered Decomposition.
تشمل البدائل مفتوحة المصدر لـ artifexsoftware/pdf2docx: pdfminer/pdfminer.six — pdfminer.six is a programmatic tool for extracting text, layout information, and metadata from PDF documents into… tabulapdf/tabula — Tabula is a PDF table extraction tool and data scraper designed to isolate tabular structures within text-based PDF… layout-parser/layout-parser — Layout-parser is a deep learning document layout parser and image analysis framework. It provides a toolkit for… pymupdf/pymupdf — PyMuPDF is a comprehensive PDF manipulation library and document analysis tool. It serves as a text extraction tool,… euske/pdfminer — pdfminer is a Python library for parsing PDF files to extract text, analyze layouts, decrypt content, and convert… camelot-dev/camelot — Camelot is a Python library and processing engine designed to extract tabular data from PDF documents. It converts…
pdfminer.six is a programmatic tool for extracting text, layout information, and metadata from PDF documents into machine-readable formats. It functions as a document parser that converts internal PDF objects and structures into accessible data objects for analysis. The project includes utilities for decrypting RC4 and AES encrypted files to enable content extraction. It also provides a layout analyzer to identify fonts, colors, and text locations to determine the organizational structure of pages. The system covers a broad range of extraction capabilities, including the retrieval of embedde
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
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
PyMuPDF is a comprehensive PDF manipulation library and document analysis tool. It serves as a text extraction tool, OCR engine, and image converter, providing a programmatic interface to edit, merge, split, and optimize PDF and Office documents. The project distinguishes itself through high-performance capabilities, including the use of C-bindings for low-level manipulation and parallelized page processing to accelerate workloads. It provides specialized conversion paths, such as transforming PDF content into Markdown for retrieval-augmented generation and large language model pipelines. It