Custom OCR Data Extraction: Receipt Parsing & Document Processing.

The primary objective of this project was to develop an advanced Optical Character Recognition (OCR) solution tailored specifically for extracting structured data from receipts, product labels, and physical documents. Businesses frequently struggle with manual data entry from physical media, which is inherently time-consuming and prone to human error. To address this bottleneck, I engineered a highly adaptable OCR system capable of processing diverse, real-world input formats—ranging from photographed retail receipts to scanned inventory labels—and seamlessly integrating that extracted data into automated business workflows.

To ensure high accuracy across varying document qualities and lighting conditions, I trained and evaluated multiple state-of-the-art models. The core engine utilized Donut and Tesseract for complex document parsing, alongside PaddleOCR, which I specifically converted into a Paddle Lite format to support lightweight, high-performance edge applications. By fine-tuning these models, the system successfully identified, isolated, and extracted critical text regions—translating transaction details on receipts and key product codes on labels into highly accurate, machine-readable formats.

The parsed data was engineered to map directly into structured relational databases, as well as exportable formats like Excel and CSV for easy cataloging and inventory management. To ensure maximum flexibility and adoption for the client, the final solutions were deployed across multiple platforms. I developed intuitive desktop and web applications, and upon request, built native mobile applications using Kotlin. This cross-platform approach enabled users to instantly snap photos of labels or scan receipts directly from their phones, automatically syncing the data to their central systems.

Ultimately, this comprehensive OCR infrastructure provided clients with a highly scalable, automated data entry pipeline. By delivering the tools across web, desktop, and mobile environments, the system was easily adopted into their day-to-day operations. This drastically reduced manual administrative overhead, minimized costly data entry errors, and significantly streamlined both their inventory management and financial tracking processes.