Browser Usage (WASM)

LiteParse ships a WebAssembly package that runs entirely in the browser — no server, no cloud calls. It supports PDF parsing and custom OCR engines implemented in JavaScript.

Install

npm install @llamaindex/liteparse-wasm

Quick start

import init, { LiteParse } from "@llamaindex/liteparse-wasm";

// Load the WASM module
await init();

const parser = new LiteParse({
  ocrEnabled: false,
  outputFormat: "json",
});

// data is a Uint8Array (e.g. from <input type="file"> or fetch)
const bytes = new Uint8Array(await file.arrayBuffer());
const result = await parser.parse(bytes);

console.log(result.text);
console.log(result.pages[0]);

What works in the browser

• PDF parsing from Uint8Array input (use file.arrayBuffer() to get bytes from a file picker for example)
• Custom OCR via the ocrEngine callback interface (see below)
• Text and JSON output formats

What doesn’t work

• File path input — pass Uint8Array instead
• DOCX/XLSX/PPTX/image conversion — requires LibreOffice/ImageMagick which aren’t available in the browser
• Built-in Tesseract or HTTP OCR — use the custom ocrEngine interface instead
• Screenshots — not available in the WASM build

OCR in the browser

The native Tesseract and HTTP OCR backends are not available in WASM. To use OCR, pass a custom ocrEngine object with a recognize method:

const parser = new LiteParse({
  ocrEnabled: true,
  ocrLanguage: "eng",
  ocrEngine: {
    /**
     * @param imageData PNG-encoded image bytes
     * @param width  rendered page width in pixels
     * @param height rendered page height in pixels
     * @param language e.g. "eng"
     * @returns array of { text, bbox: [x1, y1, x2, y2], confidence }
     */
    async recognize(imageData, width, height, language) {
      // e.g. call a Web Worker wrapping tesseract.js, or a remote OCR service
      return [
        { text: "Hello", bbox: [10, 20, 80, 40], confidence: 0.98 },
      ];
    },
  },
});

This lets you plug in any OCR implementation — a Web Worker running tesseract.js, a cloud OCR API, or anything else that returns text with bounding boxes.

Config options

All optional, camelCase:

Option	Type	Default	Description
ocrLanguage	string	"eng"	Language code passed to the OCR engine
ocrEnabled	boolean	true	Run OCR on text-sparse pages
maxPages	number	1000	Stop after this many pages
targetPages	string	—	e.g. "1-5,10,15-20"
dpi	number	150	Render DPI for OCR
outputFormat	"json" | "text"	"json"	Format used by parser.format(...)
preserveVerySmallText	boolean	false	Keep tiny text that’s normally filtered
password	string	—	Password for protected PDFs
quiet	boolean	false	Suppress progress logging
ocrEngine	object	—	Custom JS-side OCR engine (see above)