Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images

Lin, Guo-Shiang; Tu, Jia-Cheng; Lin, Jen-Yung

doi:10.3390/app11209528

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…Subsequently, a related network, Mask-RCNN [1] establishes the initial benchmark for layout segmentation in the context of instance segmentation for newspaper elements. On the other hand, RetinaNet [32] has presented yet another benchmark in convolutional techniques for detecting keywords within document images. It's important to note that this method is intricate and primarily focuses on identifying text regions.…”

Section: Convolution-based Dlamentioning

confidence: 99%

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

Banerjee,

Biswas,

Lladós

et al. 2024

IJDAR

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Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table, etc.) within a document to extract meaningful content and establish the page's layout structure. It is a crucial stage in document parsing, contributing to their comprehension. However, traditional DLA approaches often demand a significant volume of labeled training data, and the labor-intensive task of generating high-quality annotated training data poses a substantial challenge. In order to address this challenge, we proposed a semi-supervised setting that aims to perform learning on limited annotated categories by eliminating exhaustive and expensive mask annotations. The proposed setting is expected to be generalizable to novel categories as it learns the underlying positional information through a support set and class information through Co-Occurrence that can be generalized from annotated categories to novel categories. Here, we first extract features from the input image and support set with a shared multi-scale feature acquisition backbone. Then, the extracted feature representation is fed to the transformer encoder as a query. Later on, we utilize a semantic embedding network before the decoder to capture the underlying semantic relationships and similarities between different instances, enabling the model to make accurate predictions or classifications with only a limited amount of labeled data. Extensive experimentation on competitive benchmarks like PRIMA, DocLayNet, and Historical Japanese (HJ) demonstrate that this generalized setup obtains significant performance compared to the conventional supervised approach.

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Section: Convolution-based Dlamentioning

confidence: 99%

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

Banerjee,

Biswas,

Lladós

et al. 2024

IJDAR

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“…Some of them are faster and less accurate, while others have higher performances but use more computational resources, which sometimes are not suitable depending on the deployment platform. In this work, five different object detectors were used: Faster R-CNN [8], SSD [10], CenterNet [38], RetinaNet [11] and EfficientDet-D1 [39]. Table 1 shows the specific detectors evaluated in this work.…”

Section: Object Detection Pipelinesmentioning

confidence: 99%

“…Faster R-CNN is fairly well-recognized as a successful architecture for object detection, but it is not the only meta-architecture which is able to reach state-of-the-art results [9]. On the other hand, single-shot detectors, such as SSD [10] and RetinaNet [11], integrate the entire object detection process into a single neural network to generate each bounding box prediction.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery

et al. 2022

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Broccoli is an example of a high-value crop that requires delicate handling throughout the growing season and during its post-harvesting treatment. As broccoli heads can be easily damaged, they are still harvested by hand. Moreover, human scouting is required to initially identify the field segments where several broccoli plants have reached the desired maturity level, such that they can be harvested while they are in the optimal condition. The aim of this study was to automate this process using state-of-the-art Object Detection architectures trained on georeferenced orthomosaic-derived RGB images captured from low-altitude UAV flights, and to assess their capacity to effectively detect and classify broccoli heads based on their maturity level. The results revealed that the object detection approach for automated maturity classification achieved comparable results to physical scouting overall, especially for the two best-performing architectures, namely Faster R-CNN and CenterNet. Their respective performances were consistently over 80% mAP@50 and 70% mAP@75 when using three levels of maturity, and even higher when simplifying the use case into a two-class problem, exceeding 91% and 83%, respectively. At the same time, geometrical transformations for data augmentations reported improvements, while colour distortions were counterproductive. The best-performing architecture and the trained model could be tested as a prototype in real-time UAV detections in order to assist in on-field broccoli maturity detection.

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“…These techniques offer high-performance analysis support at a low cost. Deep learning (DL) methods, which work effectively using large databases, are of immense interest in the literature [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua

Chan

Doğan

et al. 2021

Entropy

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Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

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Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images

Cited by 9 publications

References 32 publications

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

SemiDocSeg: harnessing semi-supervised learning for document layout analysis

Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

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