--- title: "Best Unstructured alternatives for document parsing and ingestion (2026)" canonical_url: "https://www.nutrient.io/blog/unstructured-alternatives/" md_url: "https://www.nutrient.io/blog/unstructured-alternatives.md" last_updated: "2026-09-18T14:44:54.638Z" description: "Best Unstructured alternatives compared on output contract, source grounding, scans and tables, deployment, retrieval fit, and pricing shape." --- **TL;DR** - There’s no universal best Unstructured alternative. Choose by output contract, source grounding, scan and table handling, deployment boundary, retrieval-framework fit, and pricing shape. - Choose Nutrient when parsed pages also have to become schema-shaped fields carrying a page reference, a bounding box, and a confidence signal. - Choose Docling when document conversion has to run locally in an open source pipeline your own team operates. - Choose LlamaParse when the parsed output feeds a LlamaIndex retrieval stack. - Choose Amazon Textract, Azure AI Document Intelligence, or Google Document AI when the pipeline is already standardized on one cloud. - Keep Unstructured when connectors and chunking strategies are the center of the ingestion problem. The best Unstructured alternative depends on what happens to a document after it’s parsed. Nutrient Data Extraction API is the pick when ingestion has to end in named business fields that carry a page reference, a bounding box, and a confidence signal a reviewer can act on. Docling fits teams that need conversion to run on their own hardware. LlamaParse fits stacks already built on LlamaIndex. Reducto fits corpora where difficult visual pages are the whole problem. Marker fits a self-hosted converter that should emit Markdown and chunks. Amazon Textract, Azure AI Document Intelligence, and Google Document AI fit pipelines committed to one cloud. ## What Unstructured does well [Unstructured](https://docs.unstructured.io/api-reference/overview) is an open source Python library plus a hosted platform for turning many file types into retrieval-ready pieces. Its documentation describes partitioning a document into typed elements — titles, narrative text, list items, tables, images — each carrying metadata and coordinates, then chunking those elements with strategies such as by title, by page, and by similarity. Partitioning ranges from a fast path for born-digital files through a high-resolution path to a vision language model (VLM) path. Around that sits the part most teams actually buy: a library of source and destination [connectors and workflows](https://docs.unstructured.io/api-reference/workflow/workflows), so one pipeline can read from object storage, a content system, or a mailbox and write into a vector database. Deployment runs from the open source library on your own hardware to a managed API and a virtual private cloud (VPC) installation. That’s a coherent answer to one problem — getting varied enterprise content into a retrieval index without writing a reader for every format. ## Why teams look beyond Unstructured The reasons appear after the prototype works and the pipeline has to be operated: - The application needs named business fields with types, not elements and chunks. - A reviewer has to trace a value back to a page region before it updates a system of record. - Running the open source library in production means owning the container, its models, and its memory profile. - Documents can’t leave a specific cloud, network, or country boundary. - Users also need to view, redact, compare, or sign the same documents, and nobody wants a second vendor for that. None of these are faults in Unstructured. They’re signs that the job is no longer ingestion alone. ## Criteria that matter in production Score every candidate against the same requirements before running a sample document. ### Output contract Decide what the downstream system consumes. Markdown suits chunkers and agents because headings and tables survive as plain text. Typed elements suit pipelines that route by element type. Schema-shaped JSON suits code that expects `invoice_number` to be a string and `total_amount` to be a number. ### Source grounding and confidence Check whether each value comes back with a page reference, a bounding box, and a signal a review queue can key on. Grounding lets a reviewer see where a value came from. A confidence number is a routing signal rather than proof of correctness, and it’s worth knowing which of the two a vendor returns. ### Scans, tables, and other hard pages Born-digital documents flatter every parser. Scans, handwriting, merged cells, nested headers, and multicolumn layouts separate them. Treat table quality as a workload to measure on your own pages rather than a property of a vendor. ### Deployment and the data boundary Confirm where documents, derived artifacts, model calls, and logs live. Managed API, VPC, on-premises, and fully local are four different answers, and a vendor can support one mode for parsing and another for its model-based features. ### Retrieval-framework fit If the output feeds retrieval-augmented generation (RAG), ask how much assembly remains after parsing: chunking strategy, metadata, embedding, and the connector to the vector store. A parser hands that work back to you; an ingestion platform does it and gives you less control over the pieces. ### Pricing shape Compare the bill your workflow produces, not a headline rate. Include parsing, extraction, citations, retries, review time, and — for open source — compute and engineering ownership. Credit, per-page, per-feature, and self-hosted models aren’t comparable until they meet the same documents. ## Unstructured alternatives at a glance | Alternative | Class | Genuine strength | Grounding and output | Deployment | Choose it when | | --------------------------------------------------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------- | ----------------------------------------------------------------- | | [Nutrient Data Extraction API](https://www.nutrient.io/api/data-extraction-api/) | Grounded extraction API and document platform | Parse, extract, and classify from one API, with viewing, editing, redaction, and signing around it | Spatial elements with bounding boxes and confidence, Markdown, or schema-shaped JSON with per-field citations and match labels | Hosted API, or self-hosted through Nutrient’s SDKs and Document Engine | Parsed pages have to become reviewable fields in your own product | | [Docling](https://github.com/docling-project/docling) | Open source conversion pipeline | Local conversion with layout and table-structure models | A structured document representation exported as Markdown or JSON | Your infrastructure | Conversion has to stay on hardware you control | | [LlamaParse and LlamaExtract](https://developers.llamaindex.ai/llamaparse/) | Managed parser inside a RAG framework | Processing tiers and a direct path into LlamaIndex indexes and agents | Markdown and layout output, plus schema extraction through LlamaExtract | Managed cloud, with self-hosted and bring-your-own-cloud enterprise options | The parsed output feeds a LlamaIndex retrieval stack | | [Reducto](https://docs.reducto.ai/extract/overview) | Focused parsing and extraction platform | Schema extraction aimed at difficult layouts and complex tables | Bounding-box citations with source text and page coordinates | Cloud, hybrid VPC, or on-premises | Difficult visual documents lead the evaluation | | [Marker](https://github.com/datalab-to/marker) | Open source converter with a managed option | Tables, equations, and scanned pages, with an optional model pass | Markdown, JSON, HTML, and chunked output | Self-hosted, plus a managed Datalab platform | A self-hosted converter should emit retrieval-ready chunks | | [Azure AI Document Intelligence](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0) | Cloud document service | Prebuilt models, custom models, classification, and query fields | Strongly typed values with layout and table output | Azure | The pipeline already runs on Azure | | [Google Document AI](https://docs.cloud.google.com/document-ai/docs/processors-list) | Cloud document service | A processor catalog for forms, custom fields, and common document types | Processor-specific entities, key-value pairs, and tables | Google Cloud | The pipeline already runs on Google Cloud | | [Amazon Textract](https://docs.aws.amazon.com/textract/latest/APIReference/API_AnalyzeDocument.html) | Cloud document service | Text, forms, tables, queries, signatures, and layout as primitives | Block objects with geometry and confidence | AWS | The pipeline already runs on AWS | This isn’t a ranking. A connector-rich ingestion platform, a local converter, and a grounded extraction API solve three different problems, and one pipeline can use more than one. ## Eight alternatives to Unstructured ### 1. Nutrient Data Extraction API [Nutrient Data Extraction API](https://www.nutrient.io/api/data-extraction-api/) takes PDFs, images, and Office files and returns either typed spatial elements — paragraphs, tables, formulas, pictures, and key-value regions, each with bounding boxes, a confidence score, and reading order — or [whole-document Markdown](https://www.nutrient.io/guides/dws-data-extraction/parsing.md). A separate extract operation maps the same document to a caller-defined JSON Schema and returns each field with a page number, a bounding box, and a match label, plus a confidence signal when the engine provides one. A classify operation scores a document against labels supplied in the request and returns a ranked list, which is what an intake queue needs before it routes a file. Four processing modes set the depth: text for born-digital Markdown, structure for OCR-based spatial elements, understand for AI-augmented layout analysis, and agentic for VLM-augmented pages. The non-VLM modes give a repeatable path for the part of a corpus that doesn’t need model interpretation, and the understand and agentic paths add model-based processing for the part that does. [Confidence](https://www.nutrient.io/guides/dws-data-extraction/extract/citations-and-confidence.md) is a relative signal from zero to one rather than a probability, and the match label — `id_match`, `fuzzy_match`, `not_found` — is the interpretable field review logic keys on. The surrounding platform is the other difference. Nutrient also covers viewing, annotation, editing, redaction, comparison, conversion, and signing, and extraction can be self-hosted through Nutrient’s SDKs and Document Engine. The [Nutrient vs. Unstructured comparison](https://www.nutrient.io/api/data-extraction-api/vs/unstructured/) sets out where each fits. Choose Nutrient when parsed pages have to become reviewable, source-grounded fields inside your own product. ### 2. Docling [Docling](https://github.com/docling-project/docling) is an open source conversion pipeline that reads PDFs, Office files, images, HTML, and Markdown and produces a structured document representation it exports as Markdown or JSON. It runs locally, ships layout and table-structure models, and lets you select the optical character recognition (OCR) engine, which removes the managed service from the data path entirely. The cost moves rather than disappears. Your team owns model artifacts, hardware sizing, throughput, upgrades, and the regression tests that catch a conversion change before it reaches the index. Choose Docling when conversion has to stay on infrastructure you control and your team can own the pipeline. ### 3. LlamaParse and LlamaExtract [LlamaParse](https://developers.llamaindex.ai/llamaparse/) is LlamaIndex’s managed parser, with processing tiers that reserve deeper model work for harder pages, layout output, and a direct path into LlamaIndex indexes and agents. LlamaExtract is the adjacent service that maps a document to a caller-defined JSON Schema. LlamaIndex documents self-hosted and bring-your-own-cloud deployment for enterprise customers. Its gravity is the framework. When retrieval, indexing, and agent orchestration already run on LlamaIndex, parsing in the same ecosystem removes glue code; when they don’t, that advantage doesn’t transfer. The [LlamaParse alternatives guide](https://www.nutrient.io/blog/llamaparse-alternatives.md) covers this lane in more detail. Choose LlamaParse when the parsed output feeds a LlamaIndex retrieval stack. ### 4. Reducto [Reducto](https://docs.reducto.ai/extract/overview) is a focused parsing and extraction platform. Its documentation covers schema-based extraction, [bounding-box citations](https://docs.reducto.ai/configs/extract/citations) that carry source text and page coordinates, and [cloud, hybrid VPC, and on-premises deployment](https://docs.reducto.ai/onprem/enterprise_deployment_options). It also states that large language model (LLM) output is inherently nondeterministic and suggests caching by document hash when identical results are required. Reducto aims at getting difficult pages right rather than at moving many file types into a vector store, so ingestion plumbing stays your responsibility. The [Reducto alternatives guide](https://www.nutrient.io/blog/reducto-alternatives.md) compares that lane. Choose Reducto when difficult visual documents and deployment flexibility lead the evaluation. ### 5. Marker [Marker](https://github.com/datalab-to/marker) converts PDFs and other formats into Markdown, JSON, HTML, and chunked output. Its code is Apache 2.0 with a separate license for the model weights, it runs self-hosted, and an optional mode adds a large language model for harder pages. Datalab also offers a managed platform. It sits close to Docling in the decision, with the same tradeoff: no managed per-page fee, and no managed operations either. The chunked output is the detail worth testing. Choose Marker when a self-hosted converter should emit Markdown and chunked output ready for retrieval. ### 6. Azure AI Document Intelligence [Azure AI Document Intelligence](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview?view=doc-intel-4.0.0), formerly Form Recognizer, combines OCR and layout analysis with prebuilt models for common document types, custom template and neural models trained on labeled examples, document classification, and query fields. Responses can return strongly typed values inside Azure identity, storage, and monitoring. What it adds is model and service design: choosing between layout, prebuilt, query-field, and custom approaches, then operating the Azure resources and labeled data behind them. Choose Azure AI Document Intelligence when the pipeline already runs on Azure and the team wants prebuilt plus trainable document models. ### 7. Google Document AI [Google Document AI](https://docs.cloud.google.com/document-ai/docs/processors-list) organizes its capabilities as processors. Form Parser returns key-value pairs, checkboxes, tables, and OCR text. Custom Extractor supports user-defined fields, and pretrained processors cover common document types. [Pricing varies by processor](https://cloud.google.com/products/document-ai/pricing), so the per-page number depends on which ones the pipeline enables. Processor selection is the design decision, because language coverage, regional availability, and output shape differ between processors. Choose Google Document AI when the pipeline already runs on Google Cloud and the processor catalog covers your document types. ### 8. Amazon Textract [Amazon Textract](https://docs.aws.amazon.com/textract/latest/APIReference/API_AnalyzeDocument.html) returns Block objects for text, forms, tables, queries, signatures, and layout. Blocks carry geometry and confidence, and asynchronous operations handle multipage documents, which suits systems already built around Amazon Simple Storage Service, AWS Identity and Access Management, and queues. The tradeoff is the output contract. Downstream code has to traverse the block graph and turn it into the reading order, chunks, or field schema the application expects. Choose Amazon Textract when the pipeline already runs on AWS and managed document primitives matter more than a portable output format. ## Scenario-based recommendations Let the workload, not the vendor category, narrow the shortlist. - **Fields that trigger a decision** — Choose Nutrient when every extracted value needs a page reference, a bounding box, and a confidence signal before it updates a system of record. - **Routing before extraction** — Choose Nutrient when an intake queue has to sort mixed documents against labels you supply per request, with no training data and no template to maintain. - **A fully local pipeline** — Choose Docling when documents can’t leave hardware you control. - **A self-hosted converter for retrieval** — Choose Marker when Markdown and ready-made chunks are the output the index wants. - **Framework-native RAG** — Choose LlamaParse when LlamaIndex is already the application framework. - **Difficult visual documents** — Choose Reducto when complex tables and dense layouts dominate the corpus. - **Single-cloud architecture** — Choose Amazon Textract on AWS, Azure AI Document Intelligence on Azure, or Google Document AI on Google Cloud when native identity and monitoring outweigh output portability. - **Connector-heavy ingestion** — Keep Unstructured when the hard part is reading from many systems and writing into a vector store, and check the current status of structured data extraction on its [pricing page](https://unstructured.io/pricing) before treating it as a schema-extraction substitute. ## Run a fair proof of concept A comparison page can define the shortlist. Your documents decide the result. 1. Collect 30–50 representative files, including the worst scans and the tables people argue about. 2. Define one output contract — the same target fields, types, and missing-value rules — and hold every candidate to it. 3. Score structure and field values separately, so strong plain-text extraction can’t hide a missing required field. 4. Inspect grounding. Open the page each citation points at and confirm it’s the region a reviewer would need. 5. Repeat a sample across runs to measure variation in model-based modes. 6. Test the real deployment boundary, including authentication, storage, retention, and logs. 7. Model total operating cost with retries, review time, and engineering ownership included. The [best document parsing APIs guide](https://www.nutrient.io/blog/best-document-parsing-apis.md) and the [best PDF parsers for RAG comparison](https://www.nutrient.io/blog/best-pdf-parsers-for-rag.md) give two more views of the field. ## FAQ #### What are the best Unstructured alternatives? Nutrient Data Extraction API is the pick when ingestion has to end in schema-shaped fields with per-field citations, bounding boxes, and confidence signals. Docling and Marker are the open source converters for local pipelines, LlamaParse fits LlamaIndex-centered retrieval, Reducto fits difficult visual documents, and Amazon Textract, Azure AI Document Intelligence, and Google Document AI fit teams standardized on one cloud. Keep Unstructured when connectors and chunking are the center of the work. #### Which Unstructured alternative returns schema-shaped JSON with citations? Nutrient maps a document to a caller-defined JSON Schema and returns each field with a page number, a bounding box, and a match label, plus a confidence signal when the engine provides one. Reducto documents bounding-box citations with source text and page coordinates, and LlamaExtract offers schema-guided extraction alongside LlamaParse. Verify citations on the exact operation you plan to run. #### What is the best open source alternative to Unstructured? Nutrient publishes an open source `pdf-to-markdown` command-line tool for born-digital PDFs, which covers the simplest case without a managed service. For a broader local pipeline, Docling converts PDFs, Office files, images, and markup with layout and table-structure models, and Marker emits Markdown, JSON, HTML, and chunks under an Apache 2.0 code license. All three move operational ownership to your team. #### Which Unstructured alternative is best for RAG ingestion? Nutrient returns whole-document Markdown in its lowest-cost mode and spatial JSON with coordinates and page context when a chunker needs structure. LlamaParse is the closer fit when LlamaIndex already owns indexing and retrieval, and Docling or Marker fit teams that want conversion to run locally. Unstructured remains the fit when source and destination connectors are the harder half of the problem. #### Can I run an Unstructured alternative on-premises? Nutrient supports self-hosted processing through its SDKs and Document Engine alongside the hosted API. Docling and Marker run locally as open source software, Reducto documents hybrid VPC and on-premises deployment, and LlamaIndex offers self-hosted and bring-your-own-cloud options for enterprise customers. Validate the exact mode and deployment combination before making a compliance decision. #### How should I compare Unstructured pricing with alternatives? Nutrient charges credits per page by processing mode, and its extract operation bills a parse component plus a fixed extract component, so cost follows the depth each document needs. Cloud services usually bill per page, per processor, or per enabled feature, and open source converters move the cost into compute and engineering time. Apply every model to the same monthly page mix before comparing. ## Related reading - [Nutrient Data Extraction API vs. Unstructured](https://www.nutrient.io/api/data-extraction-api/vs/unstructured/) - [Best PDF parsers for RAG pipelines](https://www.nutrient.io/blog/best-pdf-parsers-for-rag.md) - [Best Reducto alternatives](https://www.nutrient.io/blog/reducto-alternatives.md) - [Best LlamaParse alternatives](https://www.nutrient.io/blog/llamaparse-alternatives.md) - [Best document parsing APIs](https://www.nutrient.io/blog/best-document-parsing-apis.md) --- ## Related pages - [The business case for accessibility: Five ways it drives enterprise value](/blog/5-ways-accessibility-drives-enterprise-value.md) - [Accessibility Untangled Why It Matters Guide](/blog/accessibility-untangled-why-it-matters-guide.md) - [Advanced Techniques For React Native Ui Components](/blog/advanced-techniques-for-react-native-ui-components.md) - [`vector_store` holds your indexed documents (see the multimodal RAG post](/blog/agentic-rag.md) - [How to build an AI agent for contract redlining against a compliance playbook](/blog/ai-contract-redlining-compliance-playbook.md) - [Ai Document Automation Extraction To Action](/blog/ai-document-automation-extraction-to-action.md) - [Ai Document Workflows Ocr Compliance Heavy Teams](/blog/ai-document-workflows-ocr-compliance-heavy-teams.md) - [Ai Legal Assistant Document Authoring](/blog/ai-legal-assistant-document-authoring.md) - [Amazon Textract Alternatives](/blog/amazon-textract-alternatives.md) - [Start (clears any prior buffer), navigate the document, then stop into a file.](/blog/android-faster-pdf-rendering.md) - [Android Pdf Out Of Memory Handling](/blog/android-pdf-out-of-memory-handling.md) - [Angular File Viewer Pdf Image Office Files](/blog/angular-file-viewer-pdf-image-office-files.md) - [Approval Workflow Software](/blog/approval-workflow-software.md) - [Approvals Matrix](/blog/approvals-matrix.md) - [Auto Tagging And Document Accessibility In Dotnet Sdk](/blog/auto-tagging-and-document-accessibility-in-dotnet-sdk.md) - [Simple PII redaction.](/blog/automated-pii-removal.md) - [Azure Document Intelligence Alternatives](/blog/azure-document-intelligence-alternatives.md) - [Best Ai Document Workflow Platforms](/blog/best-ai-document-workflow-platforms.md) - [Best Document Ai Platforms](/blog/best-document-ai-platforms.md) - [Best Document Classification Platforms](/blog/best-document-classification-platforms.md) - [Best document parser for RAG: LlamaParse vs. Unstructured vs. Reducto vs. Nutrient](/blog/best-document-parser-llamaparse-unstructured-reducto.md) - [Best Document Parsing Apis](/blog/best-document-parsing-apis.md) - [Best Document Viewers](/blog/best-document-viewers.md) - [Best Llm Document Understanding Platforms](/blog/best-llm-document-understanding-platforms.md) - [Best Multilingual Ocr Software](/blog/best-multilingual-ocr-software.md) - [Best Pdf Parsers For Rag](/blog/best-pdf-parsers-for-rag.md) - [Best Salesforce Document Generation Apps](/blog/best-salesforce-document-generation-apps.md) - [Best Secure Document Collaboration Platforms](/blog/best-secure-document-collaboration-platforms.md) - [Bpm Guide](/blog/bpm-guide.md) - [Bpm Tools](/blog/bpm-tools.md) - [Build Vs Buy Document Extraction](/blog/build-vs-buy-document-extraction.md) - [Business Automation](/blog/business-automation.md) - [Capex Vs Opex](/blog/capex-vs-opex.md) - [The CEO’s AI playbook: Why decision architecture beats model selection](/blog/ceo-ai-playbook-decision-architecture.md) - [1. Extract and chunk the PDF.](/blog/chat-with-pdf.md) - [Complete Guide To Pdfjs](/blog/complete-guide-to-pdfjs.md) - [Construction Document Data Extraction](/blog/construction-document-data-extraction.md) - [Convert One Drive Files To Pdf In Sharepoint](/blog/convert-one-drive-files-to-pdf-in-sharepoint.md) - [Create And Edit Pdfs In Flutter](/blog/create-and-edit-pdfs-in-flutter.md) - [Create Pdfs With React](/blog/create-pdfs-with-react.md) - [Creating A Document Scanner With Ocr In Python](/blog/creating-a-document-scanner-with-ocr-in-python.md) - [Creating And Filling Pdf Forms Programmatically In Javascript](/blog/creating-and-filling-pdf-forms-programmatically-in-javascript.md) - [The CTO’s AI playbook: Why accountability architecture beats orchestration](/blog/cto-ai-playbook-accountability-architecture.md) - [Digital Signatures](/blog/digital-signatures.md) - [Digital Workflow Automation](/blog/digital-workflow-automation.md) - [Docling Alternatives](/blog/docling-alternatives.md) - [Document Ai Vs Ocr](/blog/document-ai-vs-ocr.md) - [Document Authoring Audit Trail](/blog/document-authoring-audit-trail.md) - [Document Extraction Confidence Scores](/blog/document-extraction-confidence-scores.md) - [Document Viewer](/blog/document-viewer.md) - [Document Watermarking](/blog/document-watermarking.md) - [Emerging threats: Your logging system may be an agentic threat vector](/blog/emerging-threats-your-logging-system.md) - [Extend Alternatives](/blog/extend-alternatives.md) - [Extract Patient Data On Premises](/blog/extract-patient-data-on-premises.md) - [app.py](/blog/extract-text-from-pdf-using-python.md) - [Fillable Pdf](/blog/fillable-pdf.md) - [Google Document Ai Alternatives](/blog/google-document-ai-alternatives.md) - [How To Add Digital Signature To Pdf Using React](/blog/how-to-add-digital-signature-to-pdf-using-react.md) - [How To Build A Dotnet Maui Pdf Viewer](/blog/how-to-build-a-dotnet-maui-pdf-viewer.md) - [How To Build A Flutter Pdf Viewer](/blog/how-to-build-a-flutter-pdf-viewer.md) - [or](/blog/how-to-build-a-javascript-pdf-viewer-with-pdfjs.md) - [or](/blog/how-to-build-a-javascript-pdf-viewer.md) - [or](/blog/how-to-build-a-nextjs-pdf-viewer.md) - [How To Build A Powerpoint Viewer Using Javascript](/blog/how-to-build-a-powerpoint-viewer-using-javascript.md) - [Using Yarn](/blog/how-to-build-a-react-excel-viewer.md) - [How To Build A React Native Pdf Viewer](/blog/how-to-build-a-react-native-pdf-viewer.md) - [How To Build A React Powerpoint Viewer](/blog/how-to-build-a-react-powerpoint-viewer.md) - [How To Build A Reactjs File Viewer](/blog/how-to-build-a-reactjs-file-viewer.md) - [or](/blog/how-to-build-a-reactjs-pdf-viewer-with-react-pdf.md) - [or](/blog/how-to-build-a-reactjs-pdf-viewer.md) - [How To Build A Reactjs Viewer With Pdfjs](/blog/how-to-build-a-reactjs-viewer-with-pdfjs.md) - [How To Build A Vuejs Pdf Viewer With Pdfjs](/blog/how-to-build-a-vuejs-pdf-viewer-with-pdfjs.md) - [How To Build A Vuejs Pdf Viewer](/blog/how-to-build-a-vuejs-pdf-viewer.md) - [How To Build An Android Pdf Viewer](/blog/how-to-build-an-android-pdf-viewer.md) - [How To Build An Angular Pdf Viewer With Ng2 Pdf Viewer](/blog/how-to-build-an-angular-pdf-viewer-with-ng2-pdf-viewer.md) - [How To Build An Angular Pdf Viewer With Pdfjs](/blog/how-to-build-an-angular-pdf-viewer-with-pdfjs.md) - [How To Convert Docx To Pdf Using Javascript](/blog/how-to-convert-docx-to-pdf-using-javascript.md) - [How To Convert Docx To Pdf Using Python](/blog/how-to-convert-docx-to-pdf-using-python.md) - [How To Convert Html To Pdf Using Html2pdf](/blog/how-to-convert-html-to-pdf-using-html2pdf.md) - [or](/blog/how-to-convert-html-to-pdf-using-react.md) - [How To Convert Html To Pdf Using Wkhtmltopdf And Csharp](/blog/how-to-convert-html-to-pdf-using-wkhtmltopdf-and-csharp.md) - [or](/blog/how-to-convert-html-to-pdf-using-wkhtmltopdf-and-python.md) - [How To Convert Html To Pptx](/blog/how-to-convert-html-to-pptx.md) - [Quarterly report](/blog/how-to-convert-pdf-to-markdown-using-python.md) - [How To Convert Word To Pdf In Nodejs](/blog/how-to-convert-word-to-pdf-in-nodejs.md) - [or](/blog/how-to-create-a-react-js-signature-pad.md) - [How To Create Pdfs With React To Pdf](/blog/how-to-create-pdfs-with-react-to-pdf.md) - [How To Edit Pdfs Using Ios Pdf Library](/blog/how-to-edit-pdfs-using-ios-pdf-library.md) - [How To Embed A Pdf Viewer In Your Website](/blog/how-to-embed-a-pdf-viewer-in-your-website.md) - [How To Extract Tables From Pdf And Images](/blog/how-to-extract-tables-from-pdf-and-images.md) - [How To Generate Pdf From Html With Nodejs](/blog/how-to-generate-pdf-from-html-with-nodejs.md) - [base_url tells WeasyPrint where to resolve relative asset paths](/blog/how-to-generate-pdf-reports-from-html-in-python.md) - [How To Merge Pdfs Using Javascript](/blog/how-to-merge-pdfs-using-javascript.md) - [How To Ocr Pdfs In Linux](/blog/how-to-ocr-pdfs-in-linux.md) - [How To Print Pdf In Csharp](/blog/how-to-print-pdf-in-csharp.md) - [How To Programmatically Create And Fill Pdf Form In Angular](/blog/how-to-programmatically-create-and-fill-pdf-form-in-angular.md) - [Open an image.](/blog/how-to-use-tesseract-ocr-in-python.md) - [From an HTML string.](/blog/html-in-pdf-format.md) - [Html To Pdf In Javascript](/blog/html-to-pdf-in-javascript.md) - [Intelligent Data Extraction](/blog/intelligent-data-extraction.md) - [Invoice Approval Software](/blog/invoice-approval-software.md) - [Javascript Document Editor](/blog/javascript-document-editor.md) - [Javascript Pdf Editors](/blog/javascript-pdf-editors.md) - [Javascript Pdf Libraries](/blog/javascript-pdf-libraries.md) - [Landing Ai Alternatives](/blog/landing-ai-alternatives.md) - [Langextract Vs Llamaindex Extraction Comparison](/blog/langextract-vs-llamaindex-extraction-comparison.md) - [Linearized Pdf](/blog/linearized-pdf.md) - [Uses OpenAI by default — set OPENAI_API_KEY.](/blog/llamaindex-vs-langchain-rag.md) - [Llamaindex Workflows Vs Langgraph](/blog/llamaindex-workflows-vs-langgraph.md) - [Llamaparse Alternatives](/blog/llamaparse-alternatives.md) - [Low Code No Code Document Integrations](/blog/low-code-no-code-document-integrations.md) - [Material Requisition](/blog/material-requisition.md) - [or](/blog/merge-pdfs.md) - [Swift Package Manager](/blog/mobile-pdf-sdk.md) - [`elements` come from your document parser — each has a type and content.](/blog/multimodal-rag.md) - [Nutrient Flutter 6 Bindings Api](/blog/nutrient-flutter-6-bindings-api.md) - [Nutrient Flutter Bindings Architecture](/blog/nutrient-flutter-bindings-architecture.md) - [Nutrient Vs Conga Composer](/blog/nutrient-vs-conga-composer.md) - [Online Document Viewer](/blog/online-document-viewer.md) - [Open Pdf In Your Web App](/blog/open-pdf-in-your-web-app.md) - [PDF accessibility for developers: Meeting WCAG 2.2, Section 508, and PDF/UA with an SDK](/blog/pdf-accessibility.md) - [Extract data from PDF files: A developer guide to structured data from PDFs and scans](/blog/pdf-data-extraction-developer-guide.md) - [Pdf Extraction Benchmark Opendataloader Bench](/blog/pdf-extraction-benchmark-opendataloader-bench.md) - [Pdf Extraction Document Case Studies](/blog/pdf-extraction-document-case-studies.md) - [Pdf Page Labels](/blog/pdf-page-labels.md) - [Pdf Sdk Compliance Security Checklist](/blog/pdf-sdk-compliance-security-checklist.md) - [Pdf Sdk Performance Benchmark](/blog/pdf-sdk-performance-benchmark.md) - [Pdf Ua Compliance Guide](/blog/pdf-ua-compliance-guide.md) - [Pdf Ua Validation](/blog/pdf-ua-validation.md) - [Pdfjs Accessibility Structtree Printing](/blog/pdfjs-accessibility-structtree-printing.md) - [Pdfjs Advanced Loading Streaming Workers](/blog/pdfjs-advanced-loading-streaming-workers.md) - [Pdfjs Annotation Editor Layer](/blog/pdfjs-annotation-editor-layer.md) - [Pdfjs Area Annotations Canvas Capture](/blog/pdfjs-area-annotations-canvas-capture.md) - [Pdfjs Coordinate Systems Pdf To Screen](/blog/pdfjs-coordinate-systems-pdf-to-screen.md) - [Pdfjs Document Outline Bookmarks Metadata](/blog/pdfjs-document-outline-bookmarks-metadata.md) - [Pdfjs Eventbus Guide](/blog/pdfjs-eventbus-guide.md) - [macOS](/blog/pdfjs-file-format-conversion-to-pdf.md) - [macOS](/blog/pdfjs-generating-pdf-thumbnails-pdf2pic.md) - [Pdfjs Limitations Commercial Upgrade](/blog/pdfjs-limitations-commercial-upgrade.md) - [Pdfjs Native Annotation Layer Forms](/blog/pdfjs-native-annotation-layer-forms.md) - [Pdfjs Navigation Zoom Rotation](/blog/pdfjs-navigation-zoom-rotation.md) - [Pdfjs Pdf Page Manipulation Pdf Lib](/blog/pdfjs-pdf-page-manipulation-pdf-lib.md) - [Pdfjs React Viewer Setup](/blog/pdfjs-react-viewer-setup.md) - [Pdfjs Rendering Overlays React Portals](/blog/pdfjs-rendering-overlays-react-portals.md) - [Pdfjs Server Side Text Extraction](/blog/pdfjs-server-side-text-extraction.md) - [Pdfjs Sticky Note Annotations](/blog/pdfjs-sticky-note-annotations.md) - [Pdfjs Text Highlight Annotations](/blog/pdfjs-text-highlight-annotations.md) - [Pdfjs Text Search Pdffindcontroller](/blog/pdfjs-text-search-pdffindcontroller.md) - [Pdfjs Thumbnail Sidebar](/blog/pdfjs-thumbnail-sidebar.md) - [People Process Tools](/blog/people-process-tools.md) - [Process Flows](/blog/process-flows.md) - [React Native Pdf Annotation](/blog/react-native-pdf-annotation.md) - [React Pdf Annotation Layer Forms](/blog/react-pdf-annotation-layer-forms.md) - [React Pdf Custom Rendering Hooks](/blog/react-pdf-custom-rendering-hooks.md) - [Using Yarn](/blog/react-pdf-editor.md) - [React Pdf Loading States Errors Passwords](/blog/react-pdf-loading-states-errors-passwords.md) - [React Pdf Non Latin Fonts Special Pdfs](/blog/react-pdf-non-latin-fonts-special-pdfs.md) - [React Pdf Outline Table Of Contents](/blog/react-pdf-outline-table-of-contents.md) - [React Pdf Performance Optimization](/blog/react-pdf-performance-optimization.md) - [React Pdf Setup Basic Rendering](/blog/react-pdf-setup-basic-rendering.md) - [React Pdf Text Layer Custom Renderer](/blog/react-pdf-text-layer-custom-renderer.md) - [React Pdf Thumbnails Page Navigation](/blog/react-pdf-thumbnails-page-navigation.md) - [Reducto Alternatives](/blog/reducto-alternatives.md) - [Requisition System](/blog/requisition-system.md) - [labels.py](/blog/route-documents-automatically-classify-api.md) - [or](/blog/sample-blog-updated.md) - [Sdk Product Updates Q2 2026](/blog/sdk-product-updates-q2-2026.md) - [System Of Record Vs Source Of Truth](/blog/system-of-record-vs-source-of-truth.md) - [Add DWS MCP Server to your Claude Code project.](/blog/teaching-llms-to-read-pdfs.md) - [Open an image file.](/blog/tesseract-python-guide.md) - [The Six Best Pdf Generator Apis](/blog/the-six-best-pdf-generator-apis.md) - [Define the HTML part of the document.](/blog/top-10-ways-to-generate-pdfs-in-python.md) - [Top 5 Javascript Pdf Viewers](/blog/top-5-javascript-pdf-viewers.md) - [or](/blog/top-js-pdf-libraries.md) - [Convert an HTML file to PDF.](/blog/top-ten-ways-to-convert-html-to-pdf.md) - [Vector Pdf](/blog/vector-pdf.md) - [Wcag2 Accessibility Requirements Documents](/blog/wcag2-accessibility-requirements-documents.md) - [Web Sdk Is Now Headless](/blog/web-sdk-is-now-headless.md) - [What Are Annotations](/blog/what-are-annotations.md) - [What Is A Vpat](/blog/what-is-a-vpat.md) - [What Is Business Logic](/blog/what-is-business-logic.md) - [What Is Document Processing](/blog/what-is-document-processing.md) - [What Is Intelligent Document Processing](/blog/what-is-intelligent-document-processing.md) - [What Is Ocr Invoice Processing](/blog/what-is-ocr-invoice-processing.md) - [What Is Pdf Ua](/blog/what-is-pdf-ua.md) - [Why Pdfium Is A Trusted Platform For Pdf Rendering](/blog/why-pdfium-is-a-trusted-platform-for-pdf-rendering.md) - [Why Your Ai Agent Hallucinates Pdf Table Data](/blog/why-your-ai-agent-hallucinates-pdf-table-data.md)