Fine tuning Transformers models for converting handwritten scientific texts into LaTeX format

This study introduces a methodology for optical character recognition (OCR) that leverages transformer-based architectures to enhance the detection and recognition of textual content within images. The approach integrates state-of-the-art models, employing DETR (Detection Transformer) for the generation of bounding boxes corresponding to candidate text sequences, and TrOCR for transcribing the text contained within these regions. Both models were fine-tuned on a proprietary dataset comprising handwritten and digitized notes from mathematics-related subjects, including differential equations, calculus, linear algebra, programming, etc. The dataset predominantly consists of mathematical expressions represented in LaTeX format, thereby allowing the proposed method to effectively address the recognition of complex symbolic content in mathematical texts. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1266Dimensions.Open Alex.