Introduction to Webis Multimodal Data Cleaning Tools

Webis is an efficient data cleaning framework focusing on web content extraction, now extended to support PDF, image (PIC), and document (DOC) multimodal data cleaning systems through integration with PyMuPDF, Tesseract, and LlamaIndex. This framework provides high-quality, structured data input for large language models (LLMs), widely applied in data analysis, content aggregation, and knowledge extraction. This document details the knowledge background of the three multimodal tools and their integration methods in Webis, showcasing their technical value and implementation details.

Multimodal Tool Introduction

1. PyMuPDF (PDF Processing)

PyMuPDF is a Python library based on the MuPDF engine, known for its lightweight and efficiency since 2011, widely used for parsing and rendering PDF files. It supports extracting text, tables, images, and metadata, preserving structural information of complex documents. Webis utilizes PyMuPDF to implement page-by-page extraction functionality for PDF files, maintaining low memory usage and fast performance when processing large files such as academic papers and technical reports. Its output of structured data (such as JSON) provides precise content support for LLMs, particularly suitable for scenarios requiring document structure preservation.

2. Tesseract (Image OCR)

Tesseract is an open-source OCR engine developed by HP in 1985 and now maintained by Google, serving as the industry standard for image text extraction. It supports over 100 languages, and since version 4.0, it has introduced LSTM neural networks, significantly improving recognition accuracy, with robustness against low-quality images and complex backgrounds. Webis integrates Tesseract to process image formats (such as .png, .jpg, .jpeg), extracting text from scanned documents, screenshots, or text-containing images, supporting multilingual cleaning including Chinese, and generating structured text output.

3. LlamaIndex (DOC Document Processing)

LlamaIndex is an open-source framework developed in 2022 by a former Uber engineer, specifically designed for LLMs, focusing on document indexing and content extraction. It combines NLP technology to parse complex document structures in formats like .docx, generating semantic output. Webis leverages LlamaIndex to process DOC files, extracting key information from documents such as contracts and research reports, generating JSON data suitable for LLM training or inference, enhancing the intelligence and flexibility of document cleaning.

Integration Methods

Webis implements integration of multimodal tools through the UnifiedFileProcessor class in tools/file_processor.py, specifically including:

1. Automatic Type Recognition: According to file extensions (such as .pdf, .png, .docx), automatically call the corresponding processor.
2. Dedicated Processors: In the tools/processors/ directory, scripts for PyMuPDF, Tesseract, and LlamaIndex (such as LlamaIndex-clean.py) respectively process PDF, image, and DOC files, uniformly outputting as JSON or plain text.
3. Unified Interface: Provides API (such as extract_text_from_file) and CLI interfaces, supporting single-file and batch processing, simplifying developer operations.
4. Dependency Management: Ensures tool compatibility through setup/requirements.txt, supporting Chinese content and efficient error handling.

Future Outlook

Webis plans to expand support for more modalities (such as video and audio), further optimize cleaning performance and multilingual processing capabilities. Feedback is welcome through GitHub Issues or Pull Requests to jointly improve this multimodal data cleaning framework.