DB-QM: A Comparative Quality Measurement and Its Prospective on Persian/Arabic Databases for OCR
Seyyed Amir Hadi Minoofam, Azam Bastanfard, Mohammad Reza Keyvanpour · ACM Transactions on Asian and Low-Resource Language Information Processing · 2025
In Optical Character Recognition (OCR), state-of-the-art algorithms are applied to the same databases to compare performance and cost. Various benchmark databases have been created recently in Persian script to facilitate OCR application development. Unfortunately, there is a lack of coherent categorization and systematic identification in Persian and other languages about how to choose the databases to provide a suitable platform for evaluation. This article provides an analytical framework called DB-QM (DataBase Qualitative Measurement) to achieve a macro vision for assessing Persian OCR databases. In our proposed framework, three components are available: First, a categorization of Persian databases is proposed. Therefore, the databases are considered from their content point of view. Second, several quantitative and qualitative evaluation criteria are introduced and categorized based on the nature of databases. Finally, a discussion about the strengths and weaknesses of databases is made on the proposed criteria. It concerns a comparison among databases and the critical points about how to select one for testing algorithms. In addition, the main challenges around database improvement have been taken into account. Our analytical discussion not only clarifies the superiority of one database to another but provides a diverse discipline on how to use the appropriate databases. Also, critical challenges for the enhancement of new databases are highlighted.