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MOPO-HBT: A movie poster dataset for title extraction and recognition

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Abstract

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://ieee-dataport.org/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model.

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Data Availibility Statement

The MOPO-HBT dataset that supports the findings of this study is openly available in the public repository, IEEE Dataport, at https://www.ieee-dataport.org/documents/mopo-hbt

Notes

  1. The dataset is available on http://ieee-dataport.org/11564

  2. https://www.teaser-trailer.com, visited on 14.01.2022

  3. https://www.yts.mx, visited on 12.01.2022

  4. https://www.imdb.com/, visited on 12.01.2022

  5. http://www.impawards.com/, visited on 15.01.2022

  6. https://www.incinemas.sg, visited on 22.02.2022

  7. https://www.movieinsider.com/, visited on 12.12.2021

  8. https://www.koimoi.com/, visited on 16.01.2021

  9. https://www.in.ign.com, visited on 13.01.2022

  10. https://www.github.com/mridulxyz/MOPO/blob/main/Angle%20extraction

  11. https://www.github.com/mridulxyz/MOPO/blob/main/Coordinate%20extraction

  12. Obtained from Python library

  13. https://www.github.com/mridulxyz/MOPO/commit/229561ffc37355fffb7775e8a67583ab30a78c83

Abbreviations

MOPO-HBT :

MOvie POsters-Hollywood Bollywood Tollywood

CNN :

Convolutional Neural Network

RRC :

Robust Reading Competitions

M-EAST :

Modified- Efficient and Accurate Scene Text

ICDAR :

International Conference on Document Analysis and Recognition

SVT :

Street View Text

CUTE80 :

Curve Text 80

COCO-Text :

Common Objects in Context-Text

CVSI-15 :

Competition on Video Script Identification-15

SVM :

Support Vector Machine

RFN :

Refined Feature Attentive Network

ViTSTR :

Vision Transformer for Scene Text Recognition

BiFPN :

Bi-directional Feature Pyramid Network

EAST :

Efficient and Accurate Scene Text

TexRNet :

Text Refinement Network

VGG-19 :

Visual Geometry Group-19

ROI :

Region of Interest

IMDB :

Internet Movie Database

YTS :

YIFI Torrent Site

IMP :

Independent Moving Pictures Company

IGN :

Imagine Games Network

CUDA :

Compute Unified Device Architecture

ROC :

Receiver Operating Characteristic

AUC :

Area Under the Curve

UOI :

Union Over Intersection

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Acknowledgements

The authors acknowledge that they have collected the images from various sources: IMDB, Teaser-Trailer, YTS, IMP awards, Incinemas, Movie Insider, koimoi, and IGN.

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Authors and Affiliations

  1. Dept. of Computer Science, Shyampur Siddheswari Mahavidyalaya, Howrah, India

    Mridul Ghosh

  2. Dept. of Computer Science & Engineering, Aliah University, Kolkata, India

    Mridul Ghosh & Sk Md Obaidullah

  3. Dept. of Electronics & Electrical Communication Engineering, IIT Kharagpur, Kharagpur, India

    Sayan Saha Roy

  4. TISA Lab, Dept. of Computer Science, West Bengal State University, Barasat, India

    Bivan Banik, Himadri Mukherjee & Kaushik Roy

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  1. Mridul Ghosh
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Correspondence to Kaushik Roy.

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Appendix

Appendix

Fig. 7
figure 7

Navigating Complexity: a glimpse of challenging text extraction scenarios in the MOPO dataset

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Ghosh, M., Roy, S.S., Banik, B. et al. MOPO-HBT: A movie poster dataset for title extraction and recognition. Multimed Tools Appl 83, 54545–54568 (2024). https://doi.org/10.1007/s11042-023-17539-4

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