Building a browser-based receipt scanner with LiteRT.js

LiteRT.js is Google’s browser runtime for executing standard TensorFlow Lite, now LiteRT, .tflite models locally through WebAssembly, WebGPU, and eventually WebNN, enabling hardware-accelerated inference without converting models into JavaScript-specific formats. The tutorial demonstrates a React and TypeScript receipt scanner that preprocesses images, uses local OCR models to detect and recognize text, reconstructs receipt lines from spatial coordinates, and optionally employs an on-device Gemma model through LiteRT-LM to convert extracted content into structured JSON. It emphasizes shared, cached runtime initialization, GPU-device interoperability with TensorFlow.js, compilation fallbacks, tensor sharing to reduce CPU-GPU transfers, and lazy loading of the larger language-model runtime. The application preserves privacy by keeping receipt images and extracted text on the device, includes a regex parser fallback if LLM processing fails, and notes that the demonstration recognition model has limited accuracy but can be replaced without altering the pipeline. LiteRT.js is positioned alongside TensorFlow.js and ONNX Runtime Web as a useful choice for projects already based on .tflite models or Google’s AI Edge ecosystem, rather than as a complete replacement for TensorFlow.js.

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