Upload folder using huggingface_hub

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+MSRA_TD500_Readme.doc filter=lfs diff=lfs merge=lfs -text

MSRA_TD500_Readme.doc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb8361d8616aebf50e2d152d09a81b846f09c3017433a1f2acb0826a59a8b30a
+size 6935040

README.md

@@ -0,0 +1,53 @@
+
+# MSRA Text Detection 500 Database (MSRA-TD500)
+
+The MSRA Text Detection 500 Database (MSRA-TD500) is a publicly released benchmark designed to evaluate text detection algorithms. This dataset aims to track recent progresses in the field of text detection within natural images, particularly focusing on texts of arbitrary orientations.
+
+## Dataset Overview
+
+MSRA-TD500 contains 500 natural images sourced from indoor (e.g., office and mall) and outdoor (e.g., street) scenes captured with a pocket camera. The images depict various elements such as:
+
+- **Indoor**: Signs, doorplates, and caution plates.
+- **Outdoor**: Guide boards and billboards, often set against complex backgrounds.
+
+Images resolutions range from 1296x864 to 1920x1280. This dataset challenges users with the diversity of texts and complexity of backgrounds, featuring texts in different languages (Chinese, English, or both), fonts, sizes, colors, and orientations. Backgrounds may include elements like vegetation and repeated patterns that can be difficult to distinguish from text.
+
+## Example Images
+
+![Typical images from MSRA-TD500](example_image_path.png)
+*Figure 1: Typical images from MSRA-TD500 showing texts labeled as difficult due to factors like blur or occlusion.*
+
+## Dataset Structure
+
+The dataset is split into two sets:
+- **Training Set**: 300 images randomly selected from the original dataset.
+- **Test Set**: 200 images.
+
+All images are fully annotated, with the primary unit of annotation being the text line. This differs from the ICDAR datasets, which use the word as the basic unit.
+
+## Ground Truth Annotation
+
+Ground truth generation involves locating and bounding each text line using a four-vertex polygon, followed by fitting a minimum area rectangle around the polygon.
+
+![Ground truth generation](ground_truth_image_path.png)
+*Figure 2: Ground truth generation process.*
+
+## Evaluation Protocol
+
+The evaluation protocol, designed to accommodate texts of arbitrary orientations, uses minimum area rectangles for tighter fitting. Texts labeled as "difficult" include additional challenges like small size, occlusion, blur, or truncation. Detection misses of such texts are not penalized.
+
+## Ground Truth File Format
+
+Each image has a corresponding ground truth file. Each line in the file provides details about one text line, marking "difficult" texts with a label.
+
+```
+# Ground Truth Format Example
+Index; Text Coords; Difficulty
+0; x1,y1,x2,y2,x3,y3,x4,y4; 0
+```
+
+*Figure 3: Illustration of the ground truth file format.*
+
+## Reference
+
+C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu. "Detecting Texts of Arbitrary Orientations in Natural Images." CVPR 2012.

test/IMG_0059.gt

@@ -0,0 +1,2 @@
+0 0 300 675 1232 112 0.053651
+1 0 208 801 1489 178 0.101044

test/IMG_0080.gt

@@ -0,0 +1,2 @@
+0 0 439 670 286 94 -0.320308
+1 0 855 391 940 129 -0.326279

test/IMG_0103.gt

@@ -0,0 +1,3 @@
+0 0 610 285 177 109 -0.076043
+1 0 605 403 245 133 -0.090659
+2 0 617 553 175 75 -0.140250

test/IMG_0156.gt

@@ -0,0 +1 @@
+0 0 706 335 354 124 -0.072969

test/IMG_0158.gt

@@ -0,0 +1 @@
+0 0 502 385 297 54 -0.061989

test/IMG_0172.gt

@@ -0,0 +1,7 @@
+0 0 584 473 326 32 -0.123971
+1 0 425 524 669 67 -0.113492
+2 0 538 625 449 27 -0.109650
+3 0 549 678 380 35 -0.115388
+4 0 962 652 27 32 -0.170735
+5 0 738 726 74 37 -0.138006
+6 1 478 24 503 396 -0.034248

test/IMG_0445.gt

@@ -0,0 +1,7 @@
+0 0 730 437 136 22 -0.362544
+1 0 753 466 80 13 -0.325970
+2 0 731 506 100 24 -0.321750
+3 0 713 786 87 15 -0.171149
+4 0 684 802 152 17 -0.166282
+5 1 648 872 83 9 -0.147078
+6 1 725 873 99 10 -0.143340

test/IMG_0449.gt

@@ -0,0 +1,3 @@
+0 0 815 581 111 26 0.195581
+1 0 815 616 119 21 0.153945
+2 0 828 667 108 30 0.105377

test/IMG_0461.gt

@@ -0,0 +1,3 @@
+0 0 825 409 169 52 0.212432
+1 0 798 467 224 45 0.155868
+2 0 851 579 160 52 0.087028

test/IMG_0462.gt

@@ -0,0 +1,2 @@
+0 0 1035 402 262 43 -0.101610
+1 0 1078 517 139 41 -0.044089

test/IMG_0468.gt

@@ -0,0 +1,3 @@
+0 0 143 335 413 59 0.447971
+1 0 1156 962 349 49 0.516231
+2 0 1125 1057 299 43 0.525877

test/IMG_0475.gt

@@ -0,0 +1,8 @@
+0 1 229 638 66 11 0.285118
+1 0 105 387 156 38 0.186979
+2 0 340 425 222 42 0.153708
+3 0 678 479 327 52 0.166156
+4 0 493 720 94 17 0.293589
+5 0 871 839 131 17 0.292203
+6 0 1333 566 230 64 0.179464
+7 0 1442 1016 197 23 0.305623

test/IMG_0477.gt

@@ -0,0 +1,9 @@
+0 0 515 550 364 37 0.077837
+1 0 553 1258 62 15 -0.153944
+2 0 566 1257 139 37 -0.055499
+3 1 1420 1122 35 15 -0.237836
+4 0 1429 1130 70 25 -0.288587
+5 0 1601 1100 108 17 -0.180699
+6 0 1606 1120 115 18 -0.165149
+7 0 1130 589 192 33 0.076772
+8 0 1149 632 120 27 0.060272

test/IMG_0478.gt

@@ -0,0 +1,13 @@
+0 0 117 33 187 33 0.299683
+1 0 135 105 409 80 0.269849
+2 0 903 1034 116 61 -0.111683
+3 0 930 1103 67 38 -0.175592
+4 0 1183 1036 81 49 -0.129147
+5 0 1200 1092 48 34 -0.216085
+6 1 1693 545 35 18 0.211093
+7 1 1561 489 34 15 0.236849
+8 0 1564 512 73 32 0.230220
+9 0 817 489 110 40 0.144614
+10 0 814 566 319 53 0.139609
+11 0 820 628 262 40 0.093841
+12 0 827 689 267 41 0.064549

test/IMG_0482.gt

@@ -0,0 +1 @@
+0 0 672 690 84 39 0.099669

test/IMG_0485.gt

@@ -0,0 +1,4 @@
+0 0 862 563 188 44 -0.306419
+1 0 883 616 202 50 -0.261885
+2 0 980 750 185 47 -0.169625
+3 0 764 1173 379 28 0.207769

test/IMG_0491.gt

@@ -0,0 +1,6 @@
+0 0 285 1142 126 30 0.141897
+1 0 356 367 583 102 -0.116171
+2 0 337 449 838 174 -0.043036
+3 0 376 644 390 38 -0.026660
+4 0 301 1009 375 36 0.081342
+5 0 354 900 375 40 0.052729

test/IMG_0505.gt

@@ -0,0 +1,2 @@
+0 0 186 288 1219 295 0.054138
+1 0 141 630 1424 443 0.052029

test/IMG_0507.gt

@@ -0,0 +1,8 @@
+0 0 306 365 941 53 -0.517255
+1 1 476 136 308 28 -0.272369
+2 0 1030 12 223 26 -0.210122
+3 0 340 942 230 33 -0.009049
+4 0 340 980 242 32 -0.008403
+5 0 322 1128 579 43 0.072788
+6 1 321 1186 621 9 0.083945
+7 0 335 1023 323 34 0.045131

test/IMG_0509.gt

@@ -0,0 +1,3 @@
+0 0 665 544 147 95 -0.083141
+1 0 817 534 13 26 -0.135528
+2 0 587 1011 369 66 0.359776

test/IMG_0513.gt

@@ -0,0 +1,7 @@
+0 0 704 266 272 53 -0.113577
+1 0 665 332 392 50 -0.101689
+2 0 740 512 720 50 -0.113911
+3 0 759 642 647 45 -0.105689
+4 0 774 732 307 48 -0.115002
+5 0 421 940 481 26 -0.095939
+6 0 910 950 521 26 -0.109808

test/IMG_0520.gt

@@ -0,0 +1,6 @@
+0 1 813 3 119 22 0.084544
+1 0 333 367 240 86 -0.025526
+2 0 365 764 244 81 -0.188221
+3 0 764 358 183 78 -0.010989
+4 0 1090 355 142 65 0.000000
+5 0 1097 660 137 60 -0.140852

test/IMG_0521.gt

@@ -0,0 +1 @@
+0 0 171 596 1083 355 -0.295440