Build an AI-Powered Document Scanner and OCR Pipeline with Kotlin

Designing an AI-driven document scanning and optical character recognition (OCR) pipeline using Kotlin involves several stages. The architecture begins with CameraX, which handles the camera lifecycle and image analysis. ImageAnalysis is configured with a strategy to keep only the latest results, ensuring responsiveness even if OCR or image processing takes longer than the camera frame interval.

Document detection identifies the four corners of a page, represented by the DocumentCorners data class. This can be achieved using computer vision techniques or a machine learning model. Perspective correction is crucial for converting a photo taken at an angle into a flat document image. The four detected corners are used to calculate a perspective transformation, significantly enhancing OCR quality.

Image enhancement includes steps like grayscale conversion, contrast enhancement, noise reduction, sharpening, and adaptive thresholding. However, excessive processing should be avoided as it can remove characters or create artifacts.

Google ML Kit Text Recognition performs OCR on the image. The high-level flow involves converting the bitmap to an InputImage, processing it with the recognizer, and handling the result asynchronously. For production applications, it's advisable to move processing off the UI thread to prevent lag.

Extracting structured fields from the OCR output involves creating a data model, such as Receipt, to represent structured information like store name, date, and total amount. A rule-based extractor can handle predictable formats, while an AI model can transform OCR text into structured JSON for more flexible documents.

Document classification categorizes documents into types like invoices, receipts, contracts, identity documents, or business cards. A lightweight classifier can run locally, while more complex models can run on a backend. AI-based field extraction sends the OCR text to a backend model with a constrained schema, returning a validated, structured output.

Confidence scores indicate the reliability of the extracted information. ExtractedField<T> is a data class that includes the value and its confidence level. Low-confidence fields can be presented to the user for verification before proceeding.

Large images can consume significant memory, so it's recommended to capture, resize, process, and release resources efficiently. Exporting searchable PDFs involves combining the original page image with an invisible text layer, enabling users to search for recognized words without altering the visual appearance of the scanned page.

Privacy considerations are paramount. Process OCR locally when possible, encrypt stored documents, use HTTPS for network requests, avoid logging document contents, allow deletion of temporary images, and apply strict server-side authorization.

In conclusion, an AI-powered document scanner transforms pixels into structured information, enabling classification, field extraction, validation, summarization, and export of searchable documents. This architecture can be adapted for various document types, including invoices, receipts, forms, contracts, logistics documents, and business workflows.