Chaining Models: Combining Detection, OCR, and an LLM in a Single Workflow

Modern computer vision systems have evolved from making isolated predictions to creating intelligent vision pipelines that transform raw visual data into actionable intelligence through a multi-stage architecture. This involves chaining models together to perform spatial awareness, text extraction, and semantic reasoning, as demonstrated by processing a shopping receipt to extract and categorize food items. The process includes a perception layer using an object detection model to locate documents, an extraction layer with an optical character recognition (OCR) engine to convert images into text, and a reasoning layer utilizing a large language model (LLM) to apply business logic and organize information. The guide details the setup and training of a custom receipt detector, emphasizes the importance of dataset preparation, annotation, and model evaluation, and outlines the creation of a modular pipeline using Roboflow Workflows, integrating an RF-DETR object detector, OpenAI's OCR and LLM capabilities to efficiently process and analyze data.

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