Editorial illustration for Nemotron Pipeline Extracts PDFs to JSON: Text, Tables, Charts via OCR
PDF Data Extraction: Nemotron's AI OCR Breakthrough
Nemotron Pipeline Extracts PDFs to JSON: Text, Tables, Charts via OCR
PDFs are stubborn. They lock away text, tables, and charts in fixed layouts, making them a black box for retrieval systems. Until now, cracking that box meant sacrificing structure or losing visual context.
The Nemotron Pipeline does something different: it extracts PDFs into clean JSON, text chunks, table markdown, chart images, using OCR that actually works. Then it embeds every piece, text or image, into 2048-dimensional vectors. It reranks with a cross-encoder that sees both query and document.
It generates answers with citations. No black boxes. No hand-waving.
Just a modular pipeline you can run locally or in the cloud. Here’s how to build it.
Stage 1: Extraction (Nemotron page elements, table/chart extraction, and OCR) - Input: PDF files - Output: JSON with structured items: text chunks, table markdown, chart images - Runs: Library, self-hosted (Docker), and/or remote client Stage 2: Embedding (llama-nemotron-embed-vl-1b-v2) - Input: Extracted items (text, tables, chart images) - Output: 2048-dim vectors per item and original content - Key capability: Multimodal--encodes text-only, image-only, or image and text together - Runs: Locally on your GPU or remotely on NIM (soon) Stage 3: Reranking (llama-nemotron-rerank-vl-1b-v2) - Input: Top-K candidates from embedding search - Output: Ranked list (highest relevance first) - Key capability: Cross-encoder; sees (query, document, optional image) together - Runs: Locally on your GPU or remotely on NIM (soon) - Why it matters: Filters out "looks similar but wrong" results; the VLM version also sees images to verify relevance Once the processing pipeline is set up, answers can be generated: Generation (Llama-3.3-Nemotron-Super-49B) - Input: Top-ranked documents + user question - Output: Grounded, cited answer - Key capability: Follows strict system prompt to cite sources, admit uncertainty - Runs: Locally or NIM on build.nvidia.com Code for building each pipeline component Try the starting code for each part of the document processing pipeline.
You’ve just mapped a complete pipeline, extraction, embedding, reranking, generation, each stage sharpening the signal. No black boxes. No hand-waving.
Raw PDFs become grounded, cited answers. The magic isn’t magic; it’s multimodal precision at every step. Text, tables, charts, all captured.
Embeddings that see. Rerankers that double-check with vision. A generation model that knows when to say “I don’t know.” That’s not a demo.
That’s infrastructure. Now you have the code. Run it locally.
Host it on Docker. Push it to NIM. The pipeline is yours to deploy, tune, and scale.
The only question left: what will you do with answers this grounded?
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Common Questions Answered
How does the Nemotron pipeline transform PDF documents into structured data?
The Nemotron pipeline uses a two-stage approach to extract PDF content, first breaking down documents into structured elements like text chunks, markdown tables, and chart images. In Stage 1, the system performs page element detection, table and chart extraction, and OCR to create a comprehensive JSON representation of the document's contents.
What makes the Nemotron embedding model unique for document processing?
The llama-nemotron-embed-vl-1b-v2 model is a multimodal embedding solution that can encode text-only, image-only, or combined text and image items. This allows for flexible and comprehensive document understanding, creating 2048-dimensional vectors for each extracted document element.
What deployment options are available for the Nemotron PDF extraction pipeline?
The Nemotron pipeline offers multiple deployment flexibility, including running as a library, self-hosting via Docker container, or using a remote client. This versatility allows developers to integrate the PDF extraction system into various workflows and infrastructure setups.
Further Reading
- From PDFs to JSON with Quarkus and Docling — The Main Thread
- PDF documents to JSON | Best PDF to JSON API in 2026 - Unstract — Unstract
- From documents to insights: Advanced PDF parsing for RAG - KX — KX
- Convert PDFs to Markdown with Docling and Langflow — Langflow
- OpenDataLoader PDF - Fast Local PDF Parser for RAG — OpenDataLoader
