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Scene Text Detection Resources

Updates

Dec 4, 2020: Add 2 papers from CVPR2020/ECCV2020 and update corresponding tables.

• 1.Datasets
  • 1.1 Horizontal-Text Datasets
  • 1.2 Arbitrary-Quadrilateral-Text Datasets
  • 1.3 Irregular-Text Datasets
  • 1.4 Synthetic Datasets
  • 1.5 Comparison of Datasets
• 2. Summary of Scene Text Detection Resources
  • 2.1 Comparison of Methods
    • 2.1.1 Traditional Methods
    • 2.1.2 Segmentation-based Methods
    • 2.1.3 Regression-based Methods
    • 2.1.4 Hybrid Methods
  • 2.2 Detection Results
    • 2.2.1 Detection Results on Horizontal-Text Datasets
    • 2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets
    • 2.2.3 Detection Results on Irregular-Text Datasets
• 3. Survey
• 4. Evaluation
• 5. OCR Service
• 6. References and Code

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1. Datasets

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1.1 Horizontal-Text Datasets

• ICDAR 2003(IC03)：

  • Introduction: It contains 509 images in total, 258 for training and 251 for testing. Specifically, it contains 1110 text instance in training set, while 1156 in testing set. It has word-level annotation. IC03 only consider English text instance.
  • Link: IC03-download

• ICDAR 2011(IC11):

  • Introduction: IC11 is an English dataset for text detection. It contains 484 images, 229 for training and 255 for testing. There are 1564 text instance in this dataset. It provides both word-level and character-level annotation.
  • Link: IC11-download

• ICDAR 2013(IC13)：

  • Introduction: IC13 is almost the same as IC11. It contains 462 images in total, 229 for training and 233 for testing. Specifically, it contains 849 text instance in training set, while 1095 in testing set.
  • Link: IC13-download

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1.2 Arbitrary-Quadrilateral-Text Datasets

• USTB-SV1K：

  • Introduction: USTB-SV1K is an English dataset. It contains 1000 street images from Google Street View with 2955 text instance in total. It only provides word-level annotations.
  • Link: USTB-SV1K-download

• SVT：

  • Introduction: It contains 350 images with 725 English text intance in total. SVT has both character-level and word-level annotations. The images of SVT are harvested from Google Street View and have low resolution.
  • Link: SVT-download

• SVT-P：

  • Introduction: It contains 639 cropped word images for testing. Images were selected from the side-view angle snapshots in Google Street View. Therefore, most images are heavily distorted by the non-frontal view angle. It is the imporved datasets of SVT.
  • Link: SVT-P-download (Password : vnis)

• ICDAR 2015(IC15)：

  • Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 17548 text instance. It provides word-level annotations. IC15 is the first incidental scene text dataset and it only considers English words.
  • Link: IC15-download

• COCO-Text：

  • Introduction: It contains 63686 images in total, 43686 for training, 10000 for validating and 10000 for testing. Specifically, it contains 145859 cropped word images for testing, including handwritten and printed, clear and blur, English and non-English.
  • Link: COCO-Text-download

• MSRA-TD500：

  • Introduction: It contains 500 images in total. It provides text-line-level annotation rather than word, and polygon boxes rather than axis-aligned rectangles for text region annootation. It contains both English and Chinese text instance.
  • Link: MSRA-TD500-download

• MLT 2017：

  • Introduction: It contains 10000 natural images in total. It provides word-level annotation. There are 9 languages for MLT. It is a more real and complex datasets for scene text detection and recognition..
  • Link: MLT-download

• MLT 2019:

  • Introduction: It contains 18000 images in total. It provides word-level annotation. Compared to MLT, this dataset has 10 languages. It is a more real and complex datasets for scene text detection and recognition..
  • Link: MLT-2019-download

• CTW：

  • Introduction: It contains 32285 high resolution street view images of Chinese text, with 1018402 character instances in total. All images are annotated at the character level, including its underlying character type, bouding box, and 6 other attributes. These attributes indicate whether its background is complex, whether it’s raised, whether it’s hand-written or printed, whether it’s occluded, whether it’s distorted, whether it uses word-art.
  • Link: CTW-download

• RCTW-17：

  • Introduction: It contains 12514 images in total, 11514 for training and 1000 for testing. Images in RCTW-17 were mostly collected by camera or mobile phone, and others were generated images. Text instances are annotated with parallelograms. It is the first large scale Chinese dataset, and was also the largest published one by then.
  • Link: RCTW-17-download

• ReCTS：

  • Introduction: This data set is a large-scale Chinese Street View Trademark Data Set. It is based on Chinese words and Chinese text line-level labeling. The labeling method is arbitrary quadrilateral labeling. It contains 20000 images in total.
  • Link: ReCTS-download

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1.3 Irregular-Text Datasets

• CUTE80：

  • Introduction: It contains 80 high-resolution images taken in natural scenes. Specifically, it contains 288 cropped word images for testing. The dataset focuses on curved text. No lexicon is provided.
  • Link: CUTE80-download

• Total-Text：

  • Introduction: It contains 1,555 images in total. Specifically, it contains 11,459 cropped word images with more than three different text orientations: horizontal, multi-oriented and curved.
  • Link: Total-Text-download

• SCUT-CTW1500：

  • Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 10751 cropped word images for testing. Annotations in CTW-1500 are polygons with 14 vertexes. The dataset mainly consists of Chinese and English.
  • Link: CTW-1500-download

• LSVT：

  • Introduction: LSVT consists of 20,000 testing data, 30,000 training data in full annotations and 400,000 training data in weak annotations, which are referred to as partial labels. The labeled text regions demonstrate the diversity of text: horizontal, multi-oriented and curved.
  • Link: LSVT-download

• ArTs：

  • Introduction: ArT consists of 10,166 images, 5,603 for training and 4,563 for testing. They were collected with text shape diversity in mind and all text shapes have high number of existence in ArT.
  • Link: ArT-download

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1.4 Synthetic Datasets

• Synth80k :

  • Introduction: It contains 800 thousands images with approximately 8 million synthetic word instances. Each text instance is annotated with its text-string, word-level and character-level bounding-boxes.
  • Link: Synth80k-download

• SynthText :

  • Introduction: It contains 6 million cropped word images. The generation process is similar to that of Synth90k. It is also annotated in horizontal-style.
  • Link: SynthText-download

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1.5 Comparison of Datasets

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2. Summary of Scene Text Detection Resources

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2.1 Comparison of Methods

Scene text detection methods can be devided into four parts:

(a) Traditional methods;

(b) Segmentation-based methods;

(c) Regression-based methods;

(d) Hybrid methods.

It is important to notice that: (1) "Hori" stands for horizontal scene text datasets. (2) "Quad" stands for arbitrary-quadrilateral-text datasets. (3) "Irreg" stands for irregular scence text datasets. (4) "Traditional method" stands for the methods that don't rely on deep learning.

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2.1.1 Traditional Methods

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2.1.2 Segmentation-based Methods

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2.1.3 Regression-based Methods

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2.1.4 Hybrid Methods

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2.2 Detection Results

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2.2.1 Detection Results on Horizontal-Text Datasets

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2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets

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2.2.3 Detection Results on Irregular-Text Datasets

In this section, we only select those methods suitable for irregular text detection.

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3. Survey

[A] [TPAMI-2015] Ye Q, Doermann D. Text detection and recognition in imagery: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(7): 1480-1500. paper

[B] [Frontiers-Comput. Sci-2016] Zhu Y, Yao C, Bai X. Scene text detection and recognition: Recent advances and future trends[J]. Frontiers of Computer Science, 2016, 10(1): 19-36. paper

[C] [arXiv-2018] Long S, He X, Ya C. Scene Text Detection and Recognition: The Deep Learning Era[J]. arXiv preprint arXiv:1811.04256, 2018. paper

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4. Evaluation

If you are insterested in developing better scene text detection metrics, some references recommended here might be useful.

[A] Wolf, Christian, and Jean-Michel Jolion. "Object count/area graphs for the evaluation of object detection and segmentation algorithms." International Journal of Document Analysis and Recognition (IJDAR) 8.4 (2006): 280-296. paper

[B] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. ICDAR 2015 competition on robust reading. In ICDAR, pages 1156–1160, 2015. paper

[C] Calarasanu, Stefania, Jonathan Fabrizio, and Severine Dubuisson. "What is a good evaluation protocol for text localization systems? Concerns, arguments, comparisons and solutions." Image and Vision Computing 46 (2016): 1-17. paper

[D] Shi, Baoguang, et al. "ICDAR2017 competition on reading chinese text in the wild (RCTW-17)." 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017. paper

[E] Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, 1454–1459. IEEE. paper

[F] Dangla, Aliona, et al. "A first step toward a fair comparison of evaluation protocols for text detection algorithms." 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. paper

[G] He,Mengchao and Liu, Yuliang, et al. ICPR2018 Contest on Robust Reading for Multi-Type Web images. ICPR 2018. paper

[H] Liu, Yuliang and Jin, Lianwen, et al. "Tightness-aware Evaluation Protocol for Scene Text Detection" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019. paper code

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5. OCR Service

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6. References and Code

If you find any problems in our resources, or any good papers/codes we have missed, please inform us at liuchongyu1996@gmail.com. Thank you for your contribution.

Copyright

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