Document Parsing on SoloSoft

Open Parse: Visually-Driven Document Parser for LLM-Ready RAG Pipelines

Fri, 01 May 2026 00:00:00 +0000

The RAG (Retrieval-Augmented Generation) ecosystem has matured rapidly, but one bottleneck persists: garbage in, garbage out. Most document parsing tools feed raw text into LLM pipelines without understanding the document’s visual structure, producing chunks that break headings from their content, split tables across pages, and lose the semantic hierarchy that makes documents readable. Open Parse by Filimoa solves this problem at its root.

Open Parse is a visually-driven document parser that analyzes the actual layout of each page before extracting text. Rather than treating a PDF as a stream of characters, it identifies text blocks, columns, headings, table boundaries, and figure captions using computer vision techniques. The output preserves the document’s semantic structure as structured markdown, ready for chunking strategies that actually make sense for retrieval.

PaddleOCR: Baidu's Ultra-Lightweight OCR Toolkit with 80+ Language Support

Fri, 01 May 2026 00:00:00 +0000

PaddleOCR is Baidu’s industrial-grade, ultra-lightweight optical character recognition (OCR) toolkit built on the PaddlePaddle deep learning framework. As one of the most popular open-source OCR projects on GitHub, PaddleOCR has evolved through multiple major versions – now at PP-OCRv5 for text detection and recognition, PP-StructureV3 for comprehensive document parsing, and PP-ChatOCRv4 for LLM-powered document intelligence.

What sets PaddleOCR apart is its combination of accuracy, speed, and breadth. The PP-OCRv5 model achieves state-of-the-art accuracy while maintaining a model size of under 15 MB for the full detection and recognition pipeline. Support spans over 80 languages, and the toolkit includes everything from text detection and recognition to document layout analysis, table extraction, and even LLM-based question answering over documents.

PDF-Extract-Kit: Comprehensive PDF Content Extraction Toolkit

Fri, 01 May 2026 00:00:00 +0000

PDFs remain the most common format for document exchange, but extracting structured content from them is notoriously difficult. PDF-Extract-Kit, developed by OpenDataLab, combines deep learning models with traditional rule-based methods to extract text, tables, formulas, and images with remarkable accuracy.

The toolkit addresses the full spectrum of PDF extraction challenges. Scanned documents are handled with OCR, digital PDFs use direct text extraction, complex layouts are analyzed with layout detection models, and mathematical formulas are parsed with specialized equation recognition. The output is structured Markdown or JSON that preserves the document’s logical structure.

Extraction Capabilities

Content Type	Method	Accuracy
Text (digital)	Direct extraction	99%+
Text (scanned)	OCR with layout analysis	96%+
Tables	Deep learning detection + structure recognition	92%+
Formulas	LaTeX recognition from images	88%+
Images	Region detection + extraction	95%+

Extraction Pipeline

flowchart LR
 A[PDF File] --> B{Document Type?}
 B -->|Digital PDF| C[Direct Text Extraction]
 B -->|Scanned PDF| D[OCR Pipeline]
 C --> E[Layout Analysis]
 D --> E
 E --> F{Content Type}
 F -->|Text| G[Text Segment]
 F -->|Table| H[Table Structure Recognition]
 F -->|Formula| I[LaTeX Parsing]
 F -->|Image| J[Image Extraction]
 G --> K[Markdown/JSON Output]
 H --> K
 I --> K
 J --> K

The pipeline intelligently routes documents based on whether they are digital or scanned. After text extraction, layout analysis identifies different content regions, and specialized models handle each type of content independently before merging everything into a structured output.