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We use various synthetic and real datasets. More info is in Appendix F of the supplementary material. Some preprocessing scripts are included in tools/
.
Dataset | Type | Remarks |
---|---|---|
MJSynth | synthetic | Case-sensitive annotations were extracted from the image filenames |
SynthText | synthetic | Processed with crop_by_word_bb_syn90k.py |
IC13 | real | Three archives: 857, 1015, 1095 (full) |
IC15 | real | Two archives: 1811, 2077 (full) |
CUTE80 | real | [1] |
IIIT5k | real | [1] |
SVT | real | [1] |
SVTP | real | [1] |
ArT | real | [2] |
LSVT | real | [2] |
MLT19 | real | [2] |
RCTW17 | real | [2] |
ReCTS | real | [2] |
Uber-Text | real | [2] |
COCO-Text v1.4 | real | Processed with coco_text_converter.py |
COCO-Text v2.0 | real | Processed with coco_2_converter.py |
OpenVINO | real | Annotations for a subset of Open Images. Processed with openvino_converter.py . |
TextOCR | real | Annotations for a subset of Open Images. Processed with textocr_converter.py . A horizontal version can be generated by passing --rectify_pose . |
[1] Case-sensitive annotations from Long and Yao + our corrections. Processed with case_sensitive_str_datasets_converter.py
[2] Archives used as-is from Baek et al. They are included in the dataset release for convenience. Please refer to their work for more info about the datasets.
The preprocessed archives are available here: val + test + most of train, TextOCR + OpenVINO
The expected filesystem structure is as follows:
data
βββ test
β βββ ArT
β βββ COCOv1.4
β βββ CUTE80
β βββ IC13_1015
β βββ IC13_1095 # Full IC13 test set. Typically not used for benchmarking but provided here for convenience.
β βββ IC13_857
β βββ IC15_1811
β βββ IC15_2077
β βββ IIIT5k
β βββ SVT
β βββ SVTP
β βββ Uber
βββ train
β βββ real
β β βββ ArT
β β β βββ train
β β β βββ val
β β βββ COCOv2.0
β β β βββ train
β β β βββ val
β β βββ LSVT
β β β βββ test
β β β βββ train
β β β βββ val
β β βββ MLT19
β β β βββ test
β β β βββ train
β β β βββ val
β β βββ OpenVINO
β β β βββ train_1
β β β βββ train_2
β β β βββ train_5
β β β βββ train_f
β β β βββ validation
β β βββ RCTW17
β β β βββ test
β β β βββ train
β β β βββ val
β β βββ ReCTS
β β β βββ test
β β β βββ train
β β β βββ val
β β βββ TextOCR
β β β βββ train
β β β βββ val
β β βββ Uber
β β βββ train
β β βββ val
β βββ synth
β βββ MJ
β β βββ test
β β βββ train
β β βββ val
β βββ ST
βββ val
βββ IC13
βββ IC15
βββ IIIT5k
βββ SVT