Scene Text Recognition On Icdar2015
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| scene-text-recognition-with-permuted | 89.6±0.3 |
| decoupled-attention-network-for-text | 74.5 |
| show-attend-and-read-a-simple-and-strong | 69.2 |
| aon-towards-arbitrarily-oriented-text | 73.0 |
| aster-an-attentional-scene-text-recognizer | 76.1 |
| dtrocr-decoder-only-transformer-for-optical | 93.5 |
| a-glyph-driven-topology-enhancement-network | 87.6 |
| textscanner-reading-characters-in-order-for | 79.4 |
| clip4str-a-simple-baseline-for-scene-text-1 | 91.4 |
| what-is-wrong-with-scene-text-recognition | 71.8 |
| clip4str-a-simple-baseline-for-scene-text-1 | 90.8 |
| look-back-again-dual-parallel-attention | 85.5 |
| visual-semantics-allow-for-textual-reasoning-1 | 87.3 |
| clip4str-a-simple-baseline-for-scene-text-1 | 90.6 |
| an-empirical-study-of-scaling-law-for-ocr | 92.6 |
| multi-granularity-prediction-for-scene-text | 90.9 |
| vision-transformer-for-fast-and-efficient | 72.6 |
| cstr-a-classification-perspective-on-scene | 81.6 |
| safl-a-self-attention-scene-text-recognizer-1 | 77.5 |
| why-you-should-try-the-real-data-for-the | 80.2 |
| multi-modal-text-recognition-networks | 86.6 |
| diffusionstr-diffusion-model-for-scene-text | 86 |
| seed-semantics-enhanced-encoder-decoder | 80 |
| on-recognizing-texts-of-arbitrary-shapes-with | 79.0 |
| representation-and-correlation-enhanced | 82.2 |
| context-perception-parallel-decoder-for-scene | 91.7 |
| cdistnet-perceiving-multi-domain-character | 86.25 |