Text Recognition Techniques for Content Safety: Risks, Workflow, Algorithms, and Deployment Optimization

Text plays a crucial role in daily life and, in the era of short videos and content explosion, it is widely used for opinion expression, marketing, and other domains, making content safety based on AI essential to filter harmful information.

The presentation outlines four main parts: (1) examples of content risks in images, including pornography, violence, politics, vulgarity, and advertising; (2) a basic risk‑control workflow covering image input, text detection, correction, recognition, text‑model processing, and risk decision; (3) mainstream algorithms for text localization (regression‑based methods like CTPN, EAST, CRAFT and segmentation‑based methods like Pixel‑Embedding, DBNet) and recognition (CRNN, STATR‑NET, RARE, AttentionOCR); (4) deployment optimization, describing CPU/GPU modularization, performance bottleneck analysis, use of Pillow‑SIMD for decoding, SIMD‑accelerated preprocessing, model compression, TensorRT fusion, low‑precision inference, and batch‑size strategies.

Key algorithmic details include DBNet’s adaptive thresholding and its backbone‑neck FCN structure, as well as CRNN’s CNN‑RNN‑CTC architecture that simplifies the OCR pipeline and reduces annotation cost.

The Q&A section addresses differences between horizontal and vertical text, handling of handwritten and stylized fonts, dataset creation through data generation, and future directions such as arbitrary‑shape text, seal recognition, and synthetic data for complex backgrounds.

Overall, the article provides a comprehensive technical guide for building and optimizing OCR systems for content safety in real‑world production environments.