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ICSEJOURNALICSEJOURNAL

Journal Journal of Computer Science and Engineering (JCSE)Journal of Computer Science and Engineering (JCSE) Abstract

This study explores the efficacy of adaptive thresholding techniques in denoising signature images captured under varying lighting conditions. Signature images from multiple individuals were obtained in different illumination scenarios, and three prominent adaptive thresholding algorithms, namely histogram thresholding, Otsus method, and the Gaussian Mixture Model (GMM), were applied to the noisy images. The performance of each technique was rigorously evaluated using root mean square error (RMSE) and correlation coefficient metrics. The findings reveal that the Gaussian Mixture Model significantly outperformed both histogram thresholding and Otsus method, achieving superior noise reduction and better preservation of essential information. This was evidenced by lower RMSE values and higher correlation coefficients. These results suggest that the Gaussian Mixture Model is a highly effective technique for denoising signature images, particularly under varying lighting conditions. Its superior performance underscores its potential as a robust tool for enhancing the clarity and accuracy of signature verification systems. This study provides valuable insights into the application of adaptive thresholding techniques in image processing, highlighting the advantages of the Gaussian Mixture Model over traditional methods. The implications of this research are substantial for fields that rely on precise signature recognition and verification, such as banking, legal documentation, and security systems. This study specifically focuses on signature segmentation as a preprocessing step for signature verification systems. It does not directly address full document verification but aims to improve segmentation accuracy under varying lighting conditions, which is a foundational component in document authentication pipelines.

Conclusion

The findings indicate that the Gaussian Mixture Model (GMM) consistently outperforms both Histogram and Otsu thresholding algorithms across all quantitative measures.The GMM achieved the highest mean correlation coefficient, the highest SSIM value, and the lowest RMSE, confirming its superior capability in preserving structural information and reducing noise under varying illumination.These findings collectively highlight the exceptional performance of the GMM thresholding algorithm under diverse lighting conditions, making it a promising solution for image segmentation tasks.

Future Research

Berdasarkan penelitian ini, beberapa saran penelitian lanjutan dapat diajukan untuk memperluas pemahaman dan meningkatkan kinerja algoritma segmentasi citra tanda tangan. Pertama, penelitian dapat difokuskan pada pengembangan metode adaptif yang lebih cerdas dalam menangani variasi pencahayaan yang ekstrem, misalnya dengan menggabungkan informasi kontekstual dari dokumen atau memanfaatkan teknik pembelajaran mendalam untuk memprediksi kondisi pencahayaan optimal. Kedua, eksplorasi kombinasi algoritma thresholding yang berbeda, seperti menggabungkan Otsus method dengan GMM, dapat menghasilkan pendekatan hibrida yang lebih robust dan akurat. Ketiga, penelitian dapat menginvestigasi pengaruh parameter-parameter dalam algoritma GMM terhadap kinerja segmentasi, serta mengembangkan metode otomatis untuk memilih parameter yang optimal berdasarkan karakteristik citra tanda tangan.

Metadata

File size	730.08 KB
Pages	11
DMCA	Report