SUT: a new multi-purpose synthetic dataset for Farsi document image analysis

This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, such as document image classification, text detection and recognition, and information retrieval. The dataset comprises 62,453 images that have been categorized into 21 distinct classes, including identity documents featuring synthetically generated personal information superimposed on various backgrounds. The dataset also includes corresponding files with labeling information for the images. The ground-truth data is organized in CSV files containing compiled image file paths and associated information about the embedded data. To demonstrate the efficacy of the SUT dataset in DIA tasks, it was utilized for document classification (achieving an accuracy of 86% using a convolutional neural network) and OCR (achieving a CER of 0.083 and 0.072 using Tesseract and EasyOCR engines, respectively). The SUT dataset represents a valuable resource for researchers who are interested in developing and evaluating supervised models in Farsi document image analysis.



Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Optical Character Recognition (OCR) SUT Tesseract Character Error Rate (CER) 0.083 # 1
Optical Character Recognition (OCR) SUT EasyOCR Character Error Rate (CER) 0.072 # 2
Document Image Classification SUT CNN Accuracy 86% # 1


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