Editorial

Tarawneh, Ahmad S.; Celik, Ceyhun; Hassanat, Ahmad B. A.; Chetverikov, Dmitry

doi:10.3233/ida-180895

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Even though they are not invariant to rotation and geometric distortions [16], pretrained CNN models trained on massive image datasets (e.g. ImageNet) can select a deep feature vector invariant to rotation and form changes [17].…”

Section: Deep Feature Selection Techniquesmentioning

confidence: 99%

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

2021

IJATCSE

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Electronic Document Management is an essential workflow within every successful ERP implementation. The integration of these documents in their respective pipelines (e.g. OCR, data extraction) inside the ERP system for processing usually requires a previous classification step to improve the success rate. Unfortunately, due to the variation in type, size, and layout of business documents (i.e. invoices, checks, delivery forms), their classification is a challenging computer task and may need an additional data for model training. This paper investigates the Transfer Learning paradigm using different pre-trained deep models to extract useful features from scanned document images. In fact, the machine learning classifiers, such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) process the extracted features for classification. The authors compared the constructed models performances based on various metrics. To overcome the over-fitting issue and dataset imbalance, we run a crossvalidation procedure at different folds sizes (4, 6, and 8) to assess the models’ generalization ability. We also inspected the effect of dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on the overall performances and execution time. We found that the best classification rate is 97.83% achieved by combining LR, LDA, and the DenseNet121 deep model. Despite the small used dataset (546 images), this excellent performance encourages the integration of this approach in an ERP system as a separate module for document preprocessing for ERP users

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Section: Deep Feature Selection Techniquesmentioning

confidence: 99%

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

2021

IJATCSE

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“…Even though they are not invariant to rotation and geometric distortions (Lecun et al, 2015), pre-trained CNN models trained on huge images dataset (i.e. ImageNet) can extract a deep feature vector invariant to rotation and form changes (Tarawneh et al, 2018). Inception V3 (Szegedy et al, 2016) is a pre-trained deep learning model for image classification into 1000 classes.…”

Section: Techniquesmentioning

confidence: 99%

Handwritten Documents Validation Using Pattern Recognition and Transfer Learning

Jadli

Hain

Chergui

2021

International Journal of Web-Based Learning and Teaching Technologies

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Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.

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Editorial

Cited by 2 publications

References 28 publications

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

Handwritten Documents Validation Using Pattern Recognition and Transfer Learning

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