Text Detection

This is a binary dataset used for Bengali fake review detection in the paper "Bengali Fake Reviews: A Benchmark Dataset and Detection System" accepted in Neurocomputing, a journal published by Elsevier. - Annotated by 4 native Bangla speakers with more than 90% trustworthiness score. - Fleiss' Kappa Score: 0.83 ## Number of Total Data - Fake - 1339 - Non-fake - 7710 ## Class wise statistics of BFRD dataset | Statistics | Fake | Non-fake | |--------------------------|---------|-------------| | Total words | 1,55,789 | 9,27,902 | | Total unique words | 17,739 | 51,200 | | Max Review length | 693 | 1,614 | | Avg number of words | 116.35 | 120.35 | | Avg number of unique words | 84.99 | 88.42 | ## Citation If you use the datasets, please cite the following paper: @article{SHAHARIAR2024127732, title = {Bengali fake reviews: A benchmark dataset and detection system}, journal = {Neurocomputing}, pages = {127732}, year = {2024}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2024.127732}, url = {https://www.sciencedirect.com/science/article/pii/S0925231224005034}, author = {G.M. Shahariar and Md. Tanvir Rouf Shawon and Faisal Muhammad Shah and Mohammad Shafiul Alam and Md. Shahriar Mahbub}, keywords = {Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration}, abstract = {The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of the study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.} }

The UrduDoc Dataset is a benchmark dataset for Urdu text line detection in scanned documents. It is created as a byproduct of the UTRSet-Real dataset generation process. Comprising 478 diverse images collected from various sources such as books, documents, manuscripts, and newspapers, it offers a valuable resource for research in Urdu document analysis. It includes 358 pages for training and 120 pages for validation, featuring a wide range of styles, scales, and lighting conditions. It serves as a benchmark for evaluating printed Urdu text detection models, and the benchmark results of state-of-the-art models are provided. The Contour-Net model demonstrates the best performance in terms of h-mean. The UrduDoc dataset is the first of its kind for printed Urdu text line detection and will advance research in the field. It will be made publicly available for non-commercial, academic, and research purposes upon request and execution of a no-cost license agreement. To request the dataset and for more information and details about the UrduDoc , UTRSet-Real & UTRSet-Synth datasets, please refer to the Project Website of our paper "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents"

Media-Text dataset comprising images of banners, posters, covers and another images characterised for media industry. ### DATASET DESCRIPTION - 400 images - 7 744 annotated text instances - 973 annotations have been marked as illegible for the task of text recognition - 659 texts have been markes as do not care (###) for scene text detection. - Images are represented by 193 unique resolutions. Annotation Format - Each image has corresponding gt_*.txt file, which contains annotations in bounding box format (defined by 4 courners), transcription, and bool flag which determines that text is illegible for OCR. Proposed format is similar to ICDAR15 annotations. x1, x2, ..., x4, y4, transcription, OCR Flag **Example: ** 37,68,198,49,214,181,52,200,LADIES,False **Full paper: ** ResearchGate Please cite the related works in your publications if it helps your research: <br> S. Kalisz, M. Marczyk, J. Polańska, and R. Fagas, “Media-text: a media industry-based dataset for scene text detection,” in Modelling and simulation 2024. The 2024 European Simulation and Modelling Conference, M. Graña and J. D. Nuñez-Gonzalez, Eds., EUROSIS-ETI, 2024, pp. 138–144.