Scene Text Detection and Recognition Techniques
Summary
Scene text detection and recognition constitute a vibrant subfield of computer vision, concerned with localising and transcribing text that appears naturally in images and video. Detection typically involves identifying regions that contain text, overcoming challenges posed by variable lighting, complex backgrounds, arbitrary orientations and diverse scripts. Early methods relied on hand-crafted features and connected component analysis; modern approaches predominantly exploit deep convolutional networks, employing either two-stage frameworks with region proposals or one-stage detectors that predict text boundaries directly. Recognition transforms detected regions into character or word sequences, often using segmentation-free architectures that integrate convolutional feature extraction with sequence modelling via recurrent networks and connectionist temporal classification. Recent advances address multi-oriented and curved text through novel region representations, and improve efficiency with lightweight backbones and balanced decoders. End-to-end systems increasingly unite detection, tracking and recognition for video, while specialised models have been developed for cursive or non-Latin scripts. Applications span autonomous navigation, augmented reality, assistive technologies and large-scale image indexing, highlighting the global significance of robust scene text understanding.
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Scene Text Detection and Recognition Techniques publication trend
The graph below shows the total number of articles in scene text detection and recognition techniques across all publications each year (not limited to Nature Index journals).
Technical terms
Anchor box: Predefined bounding-box templates of various scales and aspect ratios used by object detectors to facilitate accurate localisation.
Convolutional–recurrent neural network (CRNN): A hybrid architecture combining convolutional layers for spatial feature extraction with recurrent layers for sequential data modelling.
Connectionist temporal classification (CTC): A training criterion and decoding strategy for sequence recognition that aligns input features with label sequences without requiring frame-level annotations.
Non-maximum suppression (NMS): A post-processing algorithm that filters overlapping detection proposals by retaining the highest-scoring boxes and discarding redundant ones.
F-measure: The harmonic mean of precision and recall, commonly employed to evaluate the balance between detection completeness and accuracy.
References
- Arbitrary-Shaped Text Detection With Adaptive Text Region Representation. IEEE Access (2020).
- R-YOLO: A Real-Time Text Detector for Natural Scenes with Arbitrary Rotation. Sensors (2021).
- Cursive Text Recognition in Natural Scene Images Using Deep Convolutional Recurrent Neural Network. IEEE Access (2022).
- FREE: A Fast and Robust End-to-End Video Text Spotter. IEEE Transactions on Image Processing (2020).
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