International Conference on Computer and Knowledge Engineering

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

Authors :

Elham Shabaninia¹ Fatemeh sadat Eslami² Ali Afkari Fahandari³ Hossein Nezamabadi-pour⁴

1- Department of Applied mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran 2- Department of Computer Engineering, Sirjan University of Technology, Sirjan, Iran 3- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 4- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Keywords :

Document Image Analysis،Farsi database،Document Classification،Optical Character Recognition

Abstract :

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, like 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 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 serves as an esteemed asset for scholars engaged in the development and assessment of supervised models in Farsi document image analysis.