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World desk4 min

How to Generate Unique QR Codes for Print at Scale: A Two-Script Workflow

A practical two-stage workflow separates promotional code generation from print-image rendering, with distinct checks for duplicate values and output accuracy.
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Generating a large print run of unique QR codes is a two-part job: first create and validate the underlying code values, then render approved values as print assets. In a 2026 account, promotion-platform backend operator mhk sameera describes issuing 1,498,385 codes with no duplicates reported. That is the author’s own platform total, not an independently audited benchmark—and “unique” refers to the promotional tokens encoded in the QR images, not a special QR format.

What “unique QR codes” means in this workflow

A QR symbol does not make its contents inherently unique. In a promotion, the value encoded in each symbol is the unique token; the application and database controls determine whether that value is issued and registered only once. A printed image can still be wrong or duplicated if assets are mismatched, files are reused, or production handling fails.

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Sameera describes a consumer promotion in which a shopper scans a code inside a product package to reach a redemption route. A human-readable token printed above the symbol gives entrants a manual-entry option. The workflow separates issuing values from making images so the tokens can be checked and approved before they become print assets.

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Stage 1: Generate and check code values

Configure a batch and export it

The first tool is a React interface where an operator chooses code length, quantity, and permitted characters. By default, it excludes visually confusable characters such as 0 and O, and 1 and I. It exports the resulting values to CSV.

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Compare with historical values, then approve

Before offering a new batch CSV, the described workflow compares it with previously issued codes. That comparison is a separate step against a static export, rather than a live database check at the moment of generation. Operators review and approve the values before moving to image rendering.

This preflight helps detect overlap with the historical list available in that export, but it is not a complete live prevention mechanism. Keeping the export current and ensuring every candidate value is checked are operational requirements; a stale or incomplete export can miss a collision.

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Stage 2: Render approved values as print assets

Use the browser for smaller batches

The second React tool reads the code values and creates a labeled PNG for each one. In the example, the QR points to a redemption route and the human-readable code appears above it. The implementation uses react-qr-code, file-saver, an SVG foreignObject, and a canvas rasterization step.

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Sameera presents browser rendering as suitable for hundreds to a few thousand codes. Downloads are triggered sequentially because browsers may block or silently fail when many downloads are started concurrently. The example also uses a fixed 300 ms setTimeout to wait for DOM rendering; the author says a ref-based render callback would be preferable, noting: “Move setTimeout to a proper ref-based render callback instead of a fixed delay.”

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  • Versatile App Editing Function: Easily add images from your phone, tablet or PC; customize your labels with 30+ fonts, 50+ frames, and 660+ icons; all free to use
  • Enhanced Image Quality: The App delivers precise image processing capabilities, resulting in a high-resolution and clear output; enhanced aesthetic appeal, formality, and richness
  • Easy to Use: The APP is completely free, requires no registration; features auto label size recognition; no ink or toner needed; create labels quickly and easily
  • Versatile Label Compatiblity: Compatible with square, round, address, cable, jewelry and file labels, to meet all your needs; ensuring a perfect fit for any project; keeps everything neat and tidy

Use headless Node for very large batches

For tens of thousands of images, the author switches to a headless Node script using the qrcode npm package to write files directly to disk. This avoids relying on browser download behavior. The account does not publish measured run times, a benchmark against other tools, or a repository URL for either script.

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Where duplicate prevention actually happens

The workflow has two distinct controls, with different roles:

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  • Batch preflight: compares new candidate values with historical issued codes in a static export before the CSV is offered. Because it is not live, it depends on the export being current and complete.
  • Database constraint: the MongoDB code field has a unique index. The author describes this as the registration-time backstop if overlapping batches are registered.

A unique database index prevents the same value from being stored twice in that indexed field; it does not prove that every printed file was generated correctly, paired with the intended code and campaign, or shipped without duplication. A robust print workflow therefore needs both value-level controls and production checks.

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Print QA: verify the files and the physical output

Before a run is approved, check that the generated assets correspond to the intended values and campaign. Then test the output through the actual production path at its intended printed size. The account does not specify a formal scan-test protocol or publish print success rates, so it does not support a claim that a particular size, printer, or material is guaranteed to scan.

  • Keep the approved CSV as the authoritative list of issued values.
  • Check filenames and any manifest against that list so each image maps to the right token and campaign.
  • Inspect representative rendered files, including the human-readable fallback text.
  • Print and scan samples at final size using the planned print process, rather than relying only on a screen preview.

What the reported scale does—and does not—show

In the article published September 30, 2026, Sameera reports 1,498,385 issued codes with zero duplicates, 107,218 registrations processed, and entrants from 233 countries of residence. The author says the platform has operated continuously since June 2024 across three markets. These are self-reported platform figures; the account does not provide an audit method or independent validation, and they are not performance or print-quality benchmarks.

An independent batch-printing reference

QR Bulk CLI is a separate public project, not one of Sameera’s two scripts. Its README describes offline inputs from text, URLs, CSV/TSV, or a pipeline, and output as PNG, SVG, or PDF label sheets. It documents a four-module quiet zone and a minimum printed module size of 0.25 mm. Its PDF output is a grid rather than a manufacturer-specific adhesive-label template; the project advises checking printer alignment and printing at 100%. These are that project’s documented options, not evidence about the original workflow or an endorsement of output quality: QR Bulk CLI README.

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