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Altered Handwritten Text Detection in Document Images Using Deep Learning

Shivakumara, Palaiahnakote; Patil, Gayatri; Gornale, Shivanand S.; Lopresti, Daniel P.

Authors

Gayatri Patil

Shivanand S. Gornale

Daniel P. Lopresti



Contributors

Gayatri Patil
Researcher

Shivanand S. Gornale
Supervisor

Daniel P. Lopresti
Supervisor

Abstract

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates ten distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches.

Citation

Shivakumara, P., Patil, G., Gornale, S. S., & Lopresti, D. P. (in press). Altered Handwritten Text Detection in Document Images Using Deep Learning. #Journal not on list,

Journal Article Type Article
Acceptance Date Jan 4, 2024
Deposit Date Jan 10, 2024
Journal International Journal on Pattern Recognition and Artificial Intelligence (IJPRAI)
Peer Reviewed Peer Reviewed
Keywords Altered Handwritten Text; Document Forgery; Feature Extraction; Deep Transfer Learning; Multiclass Classification