Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models with deep learning models.
Abstract
The automatic partitioning of handwritten signatures into clean and noisy is crucial to reliable authentication in documents. A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models. We have performed experiments on an offline signature dataset DocSign V2 comprising 2,400 grayscale signature images (50 users, equal number of clean and noisy signatures) created for this work. Eight conventional classifiers are tested on individual and combinations of features. The performance of a CNN built for the task and also four transfer learning models - EfficientNetB0, ResNet50, MobileNetV2, and DenseNet121 - are compared. The experiments show the best result of 97.5% was achieved with the HOG-LBP+AdaBoost combination. This is better than the best of deep learning models, MobileNetV2, which reached 95%. This demonstrates that, particularly for small datasets, carefully designed handcrafted features can be used to effectively classify clean and noisy signatures automatically. Moreover, the proposed system can also be used as a post-processing module to enhance signatures for other analysis or verification.
The increasing accessibility of digital image editing tools has made image manipulation easier than ever before, creating significant challenges in verifying the authenticity of digital visual content. Copy-move forgery is one of the most widely used image manipulation techniques because duplicated regions originate from the same image, making manual detection difficult. Existing forgery detection approaches often rely on handcrafted feature extraction or a single deep learning architecture, which may exhibit reduced performance when images undergo transformations such as rotation, scaling, compression, illumination variation, and noise addition. This paper presents a fusion-based lightweight deep learning framework for copy-move image forgery detection and localization. The proposed system integrates three efficient convolutional neural network architectures, namely SqueezeNet, MobileNetV2, and ShuffleNet, to extract complementary deep image features. Instead of depending on a single classifier, the extracted features are fused and classified using a Support Vector Machine (SVM), resulting in improved prediction reliability and reduced false detection. To provide visual evidence of manipulation, the framework incorporates a Discrete Cosine Transform (DCT)-based localization module that identifies duplicated image regions and highlights forged areas. The proposed model was implemented using Python with TensorFlow, OpenCV, Scikit-learn, NumPy, and Tkinter. Experimental evaluation was conducted on the MICC-F220 benchmark dataset. The fusion strategy achieved an overall detection accuracy of 90.91%, outperforming individual lightweight convolutional neural network models while maintaining low computational complexity. The proposed framework offers an efficient and practical solution for digital image authentication and is applicable to digital forensics, journalism, law enforcement, cyber security, and multimedia content verification.
K. Sumalini, K. B. Maruthiram· International Journal of Res...· 0 citations
This article presents an empirical comparison of four automatic face recognition methods: Principal Component Analysis (PCA), Local Binary Patterns (LBP), the deep neural network FaceNet, and an original hybrid approach proposed by the authors, referred to as FLLF (Feature-Level Late Fusion). The experiments were conducted on a subset of the VGGFace2 database, comprising approximately 480 classes in the training set and 60 classes in the validation set. For closed-set identification, a new test set (20%) was extracted from the training set. Classification accuracy, training and inference times, and prediction confidence distributions were evaluated for each method. The results show a clear advantage of the deep learning approaches: FaceNet achieved an accuracy of approximately 98% with only five training images per person, whereas the classical methods—PCA and LBP—reached only approximately 7% and 22%, respectively. The proposed FLLF method, which fuses FaceNet embeddings with PCA-whitened LBP descriptors at the feature level and classifies them with a calibrated linear SVM, further improved accuracy to approximately 98.5% and produced the highest prediction confidence values of all tested methods. However, calibration quality was not directly assessed using standard metrics such as expected calibration error or reliability diagrams, so this observation should be interpreted as a confidence-distribution shift rather than a formal calibration improvement. The article also discusses the theoretical foundations of each algorithm, their respective advantages and limitations, and the architecture of the software system implemented for this study.
Katarzyna Protasiuk, Khalid Saeed· Applied Sciences· 0 citations
In this paper, a comparative study between handcrafted and automated feature extraction method has been provided. The handcrafted method has been based over local binary pattern (LBP) as feature extraction technique. The histogram equalization (HE), multi-scale retinex (MSR), and a difference of Gaussian (DOG) have been used as a preprocessing technique to improve the image quality. The results of the handcrafted approach have been shown that the performance with HE is the best. In the automated part, ALEXNET has been used as convolutional neural network (CNN) architecture. The standard gradient descent with momentum (SGDM) has been used as the optimizer, because the results were better when it has been used. The results of the automated part have been shown how the layers activation functions works. In the automated part, the training and test accuracy have been evaluated and compared between different databases. The accuracy has achieved up to 100% in Face94 and Face grimace databases as the best accuracy in the CNN approach.
Lana Abdullah AL-Afeef, H. Al-Otum· International Journal of Fut...· 0 citations
The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images by using image processing techniques and a Convolutional Neural Network model trained on the MNIST dataset.
R. Rajesh, P. Sravani· International Journal for Re...· 0 citations
Reported performance in static Arabic sign alphabet recognition is often difficult to compare because duplicate images, variation across random seeds, robustness to image degradation, and deployment cost are rarely examined in the same experiment. In this study, we introduce ArSL-EdgeBench, a reproducible comparison of four image classifiers on the public ArSL2018 dataset. The initial data set comprised 54,049 images belonging to 32 classes. No partition was generated until duplication detection had been performed based on SHA-256, repeated visual hash inside the same class, and label conflict identification via perceptual hashing. The resulting audit retained 48,093 images that were split into equal training, validation, and test fixed stratified sets according to 70:15:15 proportions. Three models – a lightweight separable CNN MobileNetV3-Small, EfficientNetB0, and ResNet50 were trained using three random seeds each time. EfficientNetB0 came first and was chosen solely based on mean validation macro-F1 score. It demonstrated 94.21% +/- 0.21% mean test accuracy, 94.20% +/- 0.19% macro-F1, and 98.84% top-3 accuracy. Seed-42 variant achieved 94.44% accuracy on the clean test set. Its performance remained almost intact under small angle rotation and reduced illumination, while Gaussian noise decreased macro-F1 from 94.42% to 74.46%. Conversion of the model to Float16 TensorFlow Lite allowed reduction of its size to 8.03 MB with 93.14% accuracy retained; the median single-thread CPU latency was 4.12 ms. Overall, these results demonstrate that EfficientNetB0 is an effective accuracy-efficiency baseline for static alphabet prototypes. However, they do not create continuous translation or generalize to novel signers since signer identity information is not available in the public dataset.
Khaled Elbehiery, H. Elbehiery· SciNexuses· 0 citations
Offline handwritten Marathi character recognition is still kind of hard research problem because there is so much variability within the same class ,and between classes they can look a bit similar ,also the strokes are complex and different people write in their own style. A lot of CNN and Transformer like methods either do not really capture long range relationships well enough, or they end up being too heavy computationally, you know not so efficient. So in this paper we suggest a Hybrid Vision Mamba and Transformer (HVMT), framework for stronger offline handwritten Marathi character recognition. The HVMT idea combines the hierarchical feature extraction power of Vision Mamba, which uses selective state-space modeling, with the contextual representation learning of a smaller Transformer encoder, and inside that encoder we use Multi-Head Self-Attention. Experiments are done on the public MHCD_GIETV2 dataset, where handwritten Marathi characters are collected from writers in different age groups and with diverse writing styles. Before training the images are turned into grayscale, then normalized, resized, and also augmented, to help the model generalize better. The proposed HVMT is compared with CNN, ResNet-50, EfficientNet-B0, ConvNeXt-Tiny, Vision Transformer (ViT-B/16), Swin Transformer-Tiny, and Vision Mamba, all under the same experimental setup. Experimental results show that the proposed framework achieved accuracy 87.11% , precision 87.09% , recall 87.10% and F1-score 87.09% which is better than the compared architectures. At the same time it only uses 26.9 million parameters, 2.6 GFLOPs, and inference time 1.305 ms per image. In other words, the HVMT framework seems to strike a workable tradeoff between recognition precision and compute efficiency. Because of this it is a good fit for things like intelligent document analysis, handwritten document digitization , archival preservation , and several other Indic script recognition tasks and more.
S. Khandakhani, Sachikanta Dash, Sasmita Padhy et al.· Journal of Intelligent Decis...· 0 citations
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