| Unet and Morphology Prediction | 100.00% | 100.00% | 100.00% |
 Pixellink multi-scale Detection | 100.00% | 100.00% | 100.00% |
| SituTech_OCR | 100.00% | 100.00% | 100.00% |
| H&H Lab | 100.00% | 100.00% | 100.00% |
| HeReceipt-Rotation | 100.00% | 100.00% | 100.00% |
| CITlab Argus Textline Detection | 100.00% | 100.00% | 100.00% |
| SROIE Fourth Submission | 100.00% | 100.00% | 100.00% |
| EM_ocr | 100.00% | 100.00% | 100.00% |
| BOE_IOT_AIBD | 100.00% | 100.00% | 100.00% |
| BOE_AIoT_CTO | 100.00% | 100.00% | 100.00% |
| only PAN | 100.00% | 100.00% | 100.00% |
| Samsung Life Insurance | 100.00% | 100.00% | 100.00% |
| 58 OCR100 | 100.00% | 100.00% | 100.00% |
| Linklogis_BigData | 100.00% | 100.00% | 100.00% |
| BreSee OCR | 100.00% | 100.00% | 100.00% |
| ViTLP | 100.00% | 100.00% | 100.00% |
| Clova OCR | 100.00% | 97.67% | 98.82% |
| EAST_clip_enhance_896_giou | 97.62% | 100.00% | 98.80% |
| NetEase OCR | 97.62% | 100.00% | 98.80% |
| Textline detection | 100.00% | 97.14% | 98.55% |
| MCTPN2 | 100.00% | 95.45% | 97.67% |
| Ping An Property & Casualty Insurance Company | 97.62% | 97.62% | 97.62% |
| SCUT-DLVC-Lab-Refinement | 97.62% | 97.62% | 97.62% |
| A Single-Shot Model for Robust Text Localization | 97.62% | 97.62% | 97.62% |
| EfficientDet and EAST | 97.62% | 97.62% | 97.62% |
| EAST modified | 97.62% | 97.62% | 97.62% |
| DR Team | 97.62% | 97.62% | 97.62% |
| Gem AI - OCR Team | 97.62% | 97.62% | 97.62% |
| Linklogis_BeeAI | 97.62% | 97.62% | 97.62% |
| A modified CTPN model 1.0 | 97.62% | 97.62% | 97.62% |
| BiLSTM Based on CTPN | 97.62% | 97.37% | 97.49% |
| A Text Localization Method Based on CTPN | 97.62% | 97.22% | 97.42% |
| ICA-IVA | 97.14% | 96.74% | 96.94% |
| GREAT-OCR Shanghai University | 100.00% | 93.33% | 96.55% |
| 58CV | 97.62% | 93.18% | 95.35% |
| Granville ocr | 95.24% | 95.24% | 95.24% |
| A modified CTPN model 2.0 | 95.24% | 95.24% | 95.24% |
| IFLYTEK-textDet_v3 | 95.24% | 95.12% | 95.18% |
| Sunshine_OCR | 95.24% | 95.12% | 95.18% |
| Yolov3_Autohome | 92.86% | 95.12% | 93.98% |
| test_1.7 | 95.24% | 90.91% | 93.02% |
| Improved yolov3 model | 92.86% | 92.86% | 92.86% |
| MDetector | 90.48% | 95.00% | 92.68% |
| PhucPH | 92.86% | 90.70% | 91.76% |
| test_1.6 | 92.86% | 88.64% | 90.70% |
| Vsdnu | 90.48% | 90.48% | 90.48% |
| CTPN-SROIE | 85.71% | 88.24% | 86.96% |
| base4 | 88.10% | 84.09% | 86.05% |
| test | 88.10% | 84.09% | 86.05% |
| Task 1 - Scanned Receipt Text Localisation (Submitted by Intuit Inc.) | 89.05% | 78.15% | 83.24% |
| base1 | 88.10% | 78.72% | 83.15% |
| base2 | 88.10% | 78.72% | 83.15% |
| base3 | 88.10% | 78.72% | 83.15% |
| YOLO Text Detector | 80.00% | 80.00% | 80.00% |
| scene text detection weapon | 81.43% | 76.95% | 79.13% |
| Original CRAFT for SROIE | 80.95% | 75.36% | 78.05% |
| Unet Segmentation and Watershed | 61.90% | 60.47% | 61.18% |
| Practicing project for Scientific Research Subject (HCMUS master program) | 64.76% | 46.06% | 53.83% |
| Receipt Info Extracting Task1 zone-dividing | 2.38% | 11.11% | 3.92% |