QR code generation, PDF rendering, OCR, screenshot capture — every application eventually needs these. Here is why maintaining your own implementation is usually the wrong call.
QR code generation sounds trivial. Install a library, call a function, done. Except the library has a security vulnerability six months later. Then the library maintainer abandons it. Then your Python 3.12 upgrade breaks it. Then a new QR code standard comes out and your codes do not scan on the latest iOS.
Multiply this by every utility function in your codebase — PDF generation, image processing, OCR, screenshot capture — and you have a significant ongoing maintenance burden for code that is not your core product. Every hour spent on dependency management is an hour not spent on features.
The API model shifts this burden to the provider. When the QR code standard updates, the API updates. When a security issue is found in the PDF rendering library, the API patches it. You get the latest version automatically, with no action required on your end.
QR codes have seven error correction levels and four encoding modes (numeric, alphanumeric, byte, kanji). Most libraries default to the byte encoding mode with medium error correction, which works for simple URLs but is suboptimal for numeric data (like product IDs or ticket numbers) and does not support Japanese kanji encoding at all.
The QR code endpoint automatically selects the optimal encoding mode based on the input data, supports all error correction levels, and returns the code as SVG (infinitely scalable, no pixelation at any print size) or PNG at any specified resolution. For retail and logistics applications in Japan and China, kanji mode support is not optional.
Supported output formats
Optical Character Recognition has improved dramatically in the last five years. Modern OCR models handle handwriting, low-resolution scans, and rotated text that would have failed completely with older approaches. The practical applications are enormous: receipt parsing for expense management apps, invoice extraction for accounting automation, document digitization for legal and healthcare.
The OCR endpoint accepts JPEG, PNG, PDF, and TIFF inputs and returns structured JSON with the extracted text, bounding box coordinates for each text region, and a confidence score per word. The bounding boxes let you build document understanding applications that know not just what the text says but where it appears on the page.
For Indian applications, the OCR model supports Devanagari script (Hindi, Marathi, Sanskrit) in addition to Latin script. For Japanese applications, it handles kanji, hiragana, and katakana. Multi-script support in a single API call is something most OCR libraries cannot do cleanly.
PDF generation is one of the most reliably painful tasks in backend development. The open-source options are either limited (ReportLab, fpdf), complex to configure (WeasyPrint, wkhtmltopdf), or require a headless browser that adds 500MB to your Docker image (Puppeteer, Playwright).
The PDF generation endpoint accepts HTML with CSS and returns a PDF. You design your document in HTML — which every developer already knows — and the API handles the rendering. Custom fonts, tables, page breaks, headers and footers, watermarks — all supported via standard HTML and CSS.
Common use cases: invoice generation for e-commerce platforms, report generation for analytics dashboards, certificate generation for online courses, contract generation for legal tech. Any application that needs to produce a professional-looking document can use this endpoint.
QR codes, OCR, PDF generation, screenshots — all in one suite.
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