TE
T. Eckhardt
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This paper presents a novel approach to synthetic data generation for OCR post-correction, utilizing specific background and font variations tailored to specific timeperiods. The goal is to use synthetic data to enhance text accuracy in digitized historical documents. The proposed three-step process involves generating synthetic images that emulate the characteristics of historical documents from different years, incorporating year-specific backgrounds and fonts. Using these images, a dataset can be created. Multiple T5 sequence-to-sequence transformers are then fine-tuned on the generated dataset. The trained models demonstrate the capabilities of improving the OCR, and aligning them with the ground-truth text. The effectiveness of the approach is evaluated through various performance metrics, highlighting the benefits of using year-specific synthetic data for training. This work contributes to the field of OCR post-correction by providing a powerful framework for improving the accuracy of OCR systems in historical OCR text tasks.
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This paper presents a novel approach to synthetic data generation for OCR post-correction, utilizing specific background and font variations tailored to specific timeperiods. The goal is to use synthetic data to enhance text accuracy in digitized historical documents. The proposed three-step process involves generating synthetic images that emulate the characteristics of historical documents from different years, incorporating year-specific backgrounds and fonts. Using these images, a dataset can be created. Multiple T5 sequence-to-sequence transformers are then fine-tuned on the generated dataset. The trained models demonstrate the capabilities of improving the OCR, and aligning them with the ground-truth text. The effectiveness of the approach is evaluated through various performance metrics, highlighting the benefits of using year-specific synthetic data for training. This work contributes to the field of OCR post-correction by providing a powerful framework for improving the accuracy of OCR systems in historical OCR text tasks.
Wireless Sensor Networks(WSN's) are networks of sensor that wirelessly communicate to each other. The communication of these sensors needs to be secured to prevent leaking of potentially sensitive information of the data sent between the user, gateway and sensors. For WSN's lightweight authentication, protocols have been developed in order to provide lightweight authentication for resource constrained devices. This paper performs a comparative study of authentication protocols for WSN's. This is done by comparing the performance and examining the attack types to which a protocol is vulnerable. From this comparison, a possible improvement for newer authentication protocols is proposed. This study includes a comparative study on lightweight authentication protocols for IoT and a possible improvement for new authentication protocols is proposed.
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Wireless Sensor Networks(WSN's) are networks of sensor that wirelessly communicate to each other. The communication of these sensors needs to be secured to prevent leaking of potentially sensitive information of the data sent between the user, gateway and sensors. For WSN's lightweight authentication, protocols have been developed in order to provide lightweight authentication for resource constrained devices. This paper performs a comparative study of authentication protocols for WSN's. This is done by comparing the performance and examining the attack types to which a protocol is vulnerable. From this comparison, a possible improvement for newer authentication protocols is proposed. This study includes a comparative study on lightweight authentication protocols for IoT and a possible improvement for new authentication protocols is proposed.