Scene Text Recognition (STR) is a category of computer vision technology aimed at recognizing text content in natural scene images, with the goal of automatically detecting and recognizing text in complex environments such as street views, billboards, traffic signs, and product packaging. Unlike traditional OCR, which primarily processes scanned documents and printed text, STR must address challenges in the real world, including lighting variations, font diversity, perspective distortion, background interference, and irregular text shapes.

The development of scene text recognition builds on foundational technologies such as OCR, text detection, and sequence recognition. Early methods typically relied on manually designed features (e.g., HOG, SIFT) combined with classifiers for text recognition, but their generalization ability was limited in complex natural environments. In 2015, researchers at Huazhong University of Science and Technology proposed the CRNN (Convolutional Recurrent Neural Network) model in the paper An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition, which uses convolutional neural networks (CNNs) for image feature extraction, recurrent neural networks (RNNs) for sequence modeling, and CTC (Connectionist Temporal Classification) for end-to-end text recognition, establishing itself as a significant research contribution in the field of scene text recognition.

The core objective of STR is to predict the corresponding character sequence from text images in natural scenes. Unlike traditional methods that rely on manual feature engineering, modern STR models typically use deep neural networks to automatically learn text shapes and contextual information from images, and are evaluated on benchmark tasks such as regular text datasets (e.g., IIIT5K, SVT, IC13) and irregular text datasets (e.g., IC15, SVTP, CUTE80). With the advancement of deep learning and visual foundation models, scene text recognition has gradually evolved from CNN-RNN architectures to Transformer-based recognition methods, and further integrates visual-language model capabilities, improving the model's understanding of complex fonts, long texts, and low-quality images. Currently, STR technology is widely applied in fields such as intelligent transportation, image retrieval, document analysis, robotic vision, and visual assistance systems, making it an important technical direction for computer vision to understand textual information in the real world.

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