The translation of images containing chemical structure depictions into machine-readable representations is referred to as Optical Chemical Structure Recognition (OCSR). In the last three decades, there has been continuous development in OCSR tools , most of them being proprietary algorithms and rule-based tools . In general, rule-based tools work better with clean images, whereas slight distortions may hinder their performance . In recent years, deep-learning-based OCSR tools have been developed in conjunction with remarkable advancements in computer vision and natural language processing . While several publications have claimed to have developed tools that are capable of recognizing chemical depictions with high accuracy, most of these tools are either proprietary or entirely unavailable 16,21-23 . Among the few open-source OCSR software solutions , there is no system that combines chemical structure image segmentation, classification, and translation within a comprehensive workflow.