From Dependency to Self-Sufficiency: Efforts Building In-House OCR Capabilities with Open- Source Frameworks

In today’s data-centric landscape Optical Character Recognition (OCR) technology is vital for extracting data from various document formats. Organizations often opt for third-party OCR APIs, which can be expensive and limit customization. This paper documents our efforts to transition from third-party to in-house open-source OCR solutions, to reduce operational costs and enhance data security. The solution also incorporates barcode and QR code detection and decoding capabilities, supporting multiple formats including 1D and 2D barcodes.

The implementation of Optical Character Recognition (OCR) technology facilitates several critical downstream applications including Retrieval-Augmented Generation (RAG) for improved contextual responses in conversational AI systems and text summarization for efficient information processing. Through standardized APIs, we plan to achieve significant reduction in development time and integration complexity, enabling teams to implement OCR capabilities with minimal code changes. The paper outlines our strategic approach to developing a scalable OCR solution, including the selection of open-source frameworks, barcode detection algorithms, image quality optimization, and multilingual support implementation. Our journey demonstrates that developing a customized OCR solution can lead to significant improvements in cost-efficiency, data privacy, and operational flexibility, while also enabling advanced downstream applications.