Text Skew Detection Using Combined Entropy Algorithm

Brodić, Darko; Milivojević, Zoran N.

doi:10.5755/j01.itc.46.3.15797

Cited by 2 publications

(1 citation statement)

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“…Structure models like letters and forms gave interesting results, when classified using region-based algorithms [10]. Morphological features such as text skew and handwriting skew have been addressed using entropy algorithm [11] and projection profiling [12]. The study of documents is commonly dependent on text removed using OCR techniques [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Nasir

Khan

Yasmin

et al. 2020

Sensors

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Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

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