Template-based text field segmentation for ID documents using dynamic squeezeboxes packing.
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| Title: | Template-based text field segmentation for ID documents using dynamic squeezeboxes packing. |
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| Authors: | Zingerenko, Michael1,2 (AUTHOR) zingerenko.mv@phystech.edu, Limonova, Elena2,3 (AUTHOR) limonova@smartengines.com, Arlazarov, Vladimir V.2,3 (AUTHOR) vva@smartengines.com |
| Source: | Multimedia Tools & Applications. Jul2025, Vol. 84 Issue 23, p27311-27325. 15p. |
| Subjects: | Identification documents, Document imaging systems, Algorithms, Packing problem (Mathematics), Computer performance |
| Abstract: | In this paper, we focus on the problem of text field segmentation in identity documents. These documents, characterized by their fixed layouts, present an opportunity to apply computationally efficient template-based algorithms. We consider the Dynamic Squeezeboxes Packing method and demonstrate its integration into document recognition systems, utilizing a single sample per document type. We benchmark text field segmentation on the MIDV-2019 public dataset using standard intersection-over-union and our custom intersection-over-template metrics, while also measuring processing time. We demonstrate that Dynamic Squeezeboxes Packing maintains competitive quality compared to text in the wild methods (EAST, CRAFT) and named-entity recognition method (LayoutLMv2). A significant advantage of this method is its processing speed, averaging 9 ms per image on the x86_64 platform, which is substantially faster than EAST (980 ms), CRAFT (2030 ms), and LayoutLMv2 (2210 ms). The obtained results suggest that the considered method has strong potential as a method in document image analysis, particularly for processing identity documents. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this paper, we focus on the problem of text field segmentation in identity documents. These documents, characterized by their fixed layouts, present an opportunity to apply computationally efficient template-based algorithms. We consider the Dynamic Squeezeboxes Packing method and demonstrate its integration into document recognition systems, utilizing a single sample per document type. We benchmark text field segmentation on the MIDV-2019 public dataset using standard intersection-over-union and our custom intersection-over-template metrics, while also measuring processing time. We demonstrate that Dynamic Squeezeboxes Packing maintains competitive quality compared to text in the wild methods (EAST, CRAFT) and named-entity recognition method (LayoutLMv2). A significant advantage of this method is its processing speed, averaging 9 ms per image on the x86_64 platform, which is substantially faster than EAST (980 ms), CRAFT (2030 ms), and LayoutLMv2 (2210 ms). The obtained results suggest that the considered method has strong potential as a method in document image analysis, particularly for processing identity documents. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13807501 |
| DOI: | 10.1007/s11042-024-20162-6 |
