Open Source TypeScript PDF Software

Create and modify PDF documents in any JavaScript environment. Create PDF documents from scratch, or modify existing PDF documents. Draw text, images, and vector graphics. Embed your own fonts. Even embed and draw pages from other PDFs. Written in TypeScript and compiled to pure JavaScript with no native dependencies. Works in any JavaScript runtime, including browsers, Node, Deno, and even React Native. Add, insert, and remove pages. Split a single PDF into separate ones. Or merge multiple PDFs into a single document. Create new forms or fill and read existing fields. Checkboxes, buttons, radio groups, dropdowns, option lists, and text fields are all supported. If you aren't using a package manager, UMD modules are available on the unpkg and jsDelivr CDNs. Note that only some PDF readers can view attachments. This includes Adobe Reader, Foxit Reader, and Firefox. If you are using the CDN scripts in production, you should include a specific version number in the URL.

tinypdf is a minimal, zero-dependency PDF generation library that focuses on the core “put content on a page” use case while intentionally skipping heavyweight features. It is designed to be extremely small and approachable, making it a good fit when you want to generate real PDFs in Node/TypeScript without pulling in a large toolkit. The library supports essential primitives like writing text, drawing basic shapes, and placing JPEG images, which covers common needs such as invoices, receipts, tickets, and simple reports. It also supports clickable links so generated documents can include interactive URLs, and it can create multi-page documents with custom page sizes. A notable convenience is built-in markdown-to-PDF conversion for common structures like headers and lists, letting you go from formatted text to a PDF layout quickly.

OCRBase is a self-hostable document OCR and structured extraction system built to turn PDFs into machine-usable outputs at scale, aiming to bridge the gap between raw text extraction and production-ready pipelines. Instead of treating OCR as a one-off script, it presents an API-driven workflow where documents are submitted as jobs and processed through a queue-based architecture that can handle high throughput. The core output is designed for downstream automation, producing structured results like JSON according to user-defined schemas while also providing readable formats like Markdown for human review or indexing. It includes real-time job progress updates via WebSockets, which makes it easier to integrate into UIs, dashboards, or ingestion systems where users need feedback on long-running document processing.