Pelee-Text++: A Tiny Neural Network for Scene Text Detection

Cordova, Manuel A.; Pinto, Allan; Pedrini, Hélio; Torres, Ricardo da S.

doi:10.1109/access.2020.3043813

Cited by 14 publications

(4 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We will embed DeblurGAN-CNN with the recurrent neural networks (RNNs) [57] or vision transformers [11], [58] to recognize word and sentence images. Finally, finding the text from the natural scene images using the object detection methods [59], [60] and recognition by our DeblurGAN-CNN is also another direction we wish to pursue.…”

Section: Discussionmentioning

confidence: 99%

DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

Gonwirat

Surinta

2022

IEEE Access

View full text Add to dashboard Cite

Many problems can reduce handwritten character recognition performance, such as image degradation, light conditions, low-resolution images, and even the quality of the capture devices. However, in this research, we have focused on the noise in the character images that could decrease the accuracy of handwritten character recognition. Many types of noise penalties influence the recognition performance, for example, low resolution, Gaussian noise, low contrast, and blur. First, this research proposes a method that learns from the noisy handwritten character images and synthesizes clean character images using the robust deblur generative adversarial network (DeblurGAN). Second, we combine the DeblurGAN architecture with a convolutional neural network (CNN), called DeblurGAN-CNN. Subsequently, two state-of-the-art CNN architectures are combined with DeblurGAN, namely DeblurGAN-DenseNet121 and DeblurGAN-MobileNetV2, to address many noise problems and enhance the recognition performance of the handwritten character images. Finally, the DeblurGAN-CNN could transform the noisy characters to the new clean characters and recognize clean characters simultaneously. We have evaluated and compared the experimental results of the proposed DeblurGAN-CNN architectures with the existing methods on four handwritten character datasets: n-THI-C68, n-MNIST, THI-C68, and THCC-67. For the n-THI-C68 dataset, the DeblurGAN-CNN achieved above 98% and outperformed the other existing methods. For the n-MNIST, the proposed DeblurGAN-CNN achieved an accuracy of 97.59% when the AWGN+Contrast noise method was applied to the handwritten digits. We have evaluated the DeblurGAN-CNN on the THCC-67 dataset. The result showed that the proposed DeblurGAN-CNN achieved an accuracy of 80.68%, which is significantly higher than the existing method, approximately 10%.

show abstract

Section: Discussionmentioning

confidence: 99%

DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

Gonwirat

Surinta

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…On ICDAR 2013, a dataset from natural scene images with presence of horizontal or near horizontal text, our method reached competitive results with F-measure of 90.0% in Table 2, defeating some previous state-of-the-art methods proposed in the literatures [24,[40][41][42][43][44][45][46][47][48][49]. As can be seen from the Table 2, we have improved 1.3% in performance than the latest method [49].…”

Section: Comparison With Other Methodsmentioning

confidence: 68%

Fenet: Feature Enhancement Network for Arbitrary Direction Text Detection

Wang,

Liu,

Han

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…As shown in Table 3, we selected the results of three indicators that have been improved to above 80 when considering the overall performance. Compared with [38][39][40], our recall are increased by 3.75% 0.25% and 0.77%, respectively. Compared with PSENet-1s, we can find that this paper's method has improved recall, precision, and F-measure.…”

Section: Effects Of the Threshold λ In The Testing Phasementioning

confidence: 87%

SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Cao

2021

Sensors

View full text Add to dashboard Cite

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.

show abstract

Pelee-Text++: A Tiny Neural Network for Scene Text Detection

Cited by 14 publications

References 59 publications

DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

Fenet: Feature Enhancement Network for Arbitrary Direction Text Detection

SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Contact Info

Product

Resources

About