Abstract
This paper presents a robust method for character segmentation from coin images. While many papers studied character segmentation and recognition from structured and unstructured documents. Several methods proposed that vary, in terms of targeted documents, from complex (degraded) into different languages. This is the first paper to study and propose a solution for character segmentation from coins. Character segmentation plays a crucial role in coin recognition, grading and authentication systems. Scaling and rotating the coins are challenging in character segmentation due to the circular nature of coins. In this paper, we transform the coin from circular into rectangular shape and then perform morphological operations to compute the horizontal and vertical projection profiles and apply dynamic adaptive mask to extract characters. Our method is evaluated on several coins from diverse countries with different image background complexity. The proposed method achieved precision and recall rates as high as 93.5% and 94.8% respectively demonstrating the effectiveness of the proposed method.
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Hmood, A.K., Dittimi, T.V., Suen, C.Y. (2017). Scale and Rotation Invariant Character Segmentation from Coins. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_18
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DOI: https://doi.org/10.1007/978-3-319-59876-5_18
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