This research work compares standalone OCR with Hybrid model. Optical Character Recognition (OCR) system has been widely adopted to convert handwritten documents to a digitized, machine-readable format. This OCR system often produces error-prone outputs when handling cursive or irregular handwriting. At the same time, the emergence of Large language models, such as OpenAI’s GPT family, has enabled automated correction of textual errors while preserving meaning. This article presents OCRFlow, a novel hybrid system that combines Google Cloud Vision OCR and OpenAI’s GPT with a user-friendly graphical interface built using Python’s Tkinter. It also analyses its accuracy, latency, efficiency and usability, with the aim to design a robust system that performs more efficiently than any standalone model.

Keywords: OCR, Convolutional Neural Network, GPT, Deep Learning, Connectionist Temporal Classification, Machine Learning, Long Short Term Memory, Language Model, Transformer Model