This paper investigates the application of text recognition technologies, spanning conventional Optical Character Recognition (OCR) and vision-language models (VLMs) in civil engineering, with a focus on the structured information extraction of textual information in two-dimensional (2D) bridge engineering drawings. Pre-trained OCR tools are evaluated for recognizing printed and handwritten text fragments extracted from bridge engineering drawings, and four VLMs are assessed as OCR-like recognizers. The evaluation employs domain-specific test datasets under controlled degradations (motion blur, partial occlusion, salt-and-pepper noise) and rotations (±45°, ±90°), and adopts four metrics: Levenshtein-based similarity, Jaccard similarity, Term Frequency–Inverse Document Frequency (TF–IDF) similarity, and WordNet-based similarity. The results demonstrate that while all tools perform well on printed text, VLMs achieve the strongest overall robustness across perturbations, and Handprint consistently achieves the highest accuracy among OCR tools, particularly for handwritten text and under rotation. To further enhance the interpretability and usability of raw recognition outputs, two lightweight, rule-based post-recognition structured information extraction methods are developed: a region-based method for extracting fixed-layout information such as title blocks, and a keyword-driven method for identifying domain-specific annotations like material specifications. These methods require no prior segmentation or additional trainable models and are integrated into a graphical user interface, TextBridge. The proposed workflow offers an effective solution for the semi-automated extraction of structured textual content from engineering drawings, with the potential to facilitate the digitization and integration of legacy infrastructure data into modern applications such as Building Information Modeling (BIM) and digital twin systems.