Between Image and Text: Automatic Image Processing for Character Recognition in Historical Inscriptions

The research addresses the challenges in Optical Character Recognition (OCR) systems when applied to ancient inscriptions and graffiti. These artifacts, serving celebratory or commemorative purposes, often present legibility issues due to erosion and gaps in the text. Our study proposes an automated image processing pipeline supported by 3D data from photogrammetric surveys. The processing phase involves manipulating image parameters and utilizing spatial coordinates and writing system information. The goal is to enhance legibility by extracting images with neutral backgrounds and highlighted characters, resembling printed texts. This processed data aims to improve the performance of pre-trained Artificial Intelligence (AI) models dedicated to OCR. Ultimately, the research seeks to provide a compar-ative study between unprocessed and processed images, validating the significance of the pre-processing phase in enhancing text recognition systems. The proposed automated workflow aims to contribute to the field of computer vision, specifically in the context of preserving and interpreting historical inscriptions.