A new robust approach for altered handwritten text detection

Patil, Gayatri; Shivakumara, Palaiahnakote; Gornale, Shivanand S.; Pal, Umapada; Blumenstein, Michael

doi:10.1007/s11042-022-14242-8

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Unlike ours, this proposal considers general holograms and therefore lacks precision. Patil et al (2022) relied on sequencing the order of pixels to discover irregularities in handwritten documents. This type of document is not within the scope of this research.…”

Section: Comparison With Similar Workmentioning

confidence: 99%

Fals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF Documents

Nguinabe,

Tchakounte,

Tchakounte

et al. 2023

Journal of Computer Science

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Fake Portable Document Format (PDF) documents are disseminated in an incredible rhythm across social media. Negative incidences are obvious but effective solutions identifying falsified items in the PDF are still in need. Unlike determining malicious scripts inserted into the file, this research aims at identifying falsified objects from different layers of the document. Specifically, we introduce Fals-Ism, a novel approach to detect falsified PDF documents based on graph isomorphism. Each document is transformed and characterized by metadata, structure, and content required to build the corresponding graph such that any alteration is reflected on the complete graph. The graph is input to the isomorphism search algorithm namely; VF2 to verify if there is a similarity-based isomorphism. Experiments are conducted on (36) PDF documents considering metadata, structure, and content modifications. The results show that Fals-Ism (i) Is efficient to detect forgery at metadata level, structure, and content; (ii) Is robust and resistant to forgery attacks such as insertion, deletion, and modification of information; (iii) Does not require certain information about the PDF documents beforehand to perform the detection. Fals-Ism can detect different types of falsifications in PDF (version 1.7 or higher) with an accuracy of 90%. A comparison with similar work confirms that Fals-Ism could be a complementary tool for fake news detection.

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Section: Comparison With Similar Workmentioning

confidence: 99%

Fals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF Documents

Nguinabe,

Tchakounte,

Tchakounte

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The need for robust approaches to combat visual degradation, encompassing noise and blurriness, is constantly growing within the realm of computer vision. This need has spurred the development of image restoration techniques, dedicated to reconstructing pristine images from their corrupted counterparts [2][3][4][5][6]. In this revised version, the opening sentence clarifies the focus on denoising as a core challenge.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Deep Learning for Efficient and Noise-Robust License Plate Detection and Recognition

Shim,

Imtiaz,

Habibullah

et al. 2024

IJACSA

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Accurate license plate recognition (LPR) remains a crucial task in various applications, from traffic monitoring to security systems. However, noisy environments with challenging factors like blurred images, low light, and complex backgrounds can significantly impede traditional LPR methods. This work proposes a deep learning based LPR system optimized for performance in noisy environments through hyperparameter tuning and bounding box refinement. We first preprocessed the noisy images by noise reduction which is crucial for robust LPR. We employed Convolutional Autoencoder (CAE) trained on noisy/clean image pairs to remove noise and enhance details. We utilized the InceptionResNetV2 architecture, pre-trained on ImageNet, for its strong feature extraction capabilities. We then added Region Proposal Network (RPN) head added to InceptionResNetV2 to predict candidate bounding boxes around potential license plates. We employed grid search to optimize key hyperparameters like learning rate, optimizer settings, and RPN anchor scales, ensuring optimal model performance for the specific noise patterns in the target dataset. Non-maximum suppression (NMS) eliminates redundant proposals, and a separate detection head classifies each remaining bounding box as license plate or background. Finally, bounding boxes are refined for improved accuracy. For confirmed license plates, a Bidirectional LSTM/CRNN network extracts and decodes character sequences within the refined bounding boxes.Compared to the recent methods, the proposed approach yielded the highest detection and recognition performance in noisy environments which can be best utilized for monitoring traffic, security systems in noisy environment. Our optimized LPR system demonstrates significantly improved accuracy and robustness compared to baseline methods, particularly in noisy environments.

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MuBDA: Multimodal Biometric Data Analysis for Gender Classification Using Deep Learning Techniques

Gornale,

Kumar,

Kruthi

2024

Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology

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A new robust approach for altered handwritten text detection

Cited by 5 publications

References 24 publications

Fals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF Documents

Fals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF Documents

Optimizing Deep Learning for Efficient and Noise-Robust License Plate Detection and Recognition

MuBDA: Multimodal Biometric Data Analysis for Gender Classification Using Deep Learning Techniques

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