Selective Super-Resolution for Scene Text Images
Ryo Nakao∗ , Brian Kenji Iwana∗ , and Seiichi Uchida∗∗ Advanced Information Technology, Kyushu University, Japan{brian, uchida}@human.ait.kyushu-u.ac.jp

Abstract—In this paper, we realize the enhancement of superresolution using images with scene text. Specifically, this paper
proposes the use of Super-Resolution Convolutional Neural
Networks (SRCNN) which are constructed to tackle issues
associated with characters and text. We demonstrate thatstandard SRCNNs trained for general object super-resolution is
not sufficient and that the proposed method is a viable methodin creating a robust model for text. To do so, we analyze thecharacteristics of SRCNNs through quantitative and qualitativeevaluations with scene text data. In addition, analysis using
the correlation between layers by Singular Vector CanonicalCorrelation Analysis (SVCCA) and comparison of filters ofeach SRCNN using t-SNE is performed. Furthermore, in orderto create a unified super-resolution model specialized for bothtext and objects, a model using SRCNNs trained with thedifferent data types and Content-wise Network Fusion (CNF)
is used. We integrate the SRCNN trained for character images
and then SRCNN trained for general object images, and verify
the accuracy improvement of scene images which include text.
We also examine how each SRCNN affects super-resolution
images after fusion.
Keywords-Super-Resolution, Scene Text, Super-Resolution
Convolutional Neural Network, Context-wise Network Fusion

I. I NTRODUCTION
Super-resolution is a technique for enhancing a lowresolution image to a high-resolution image. Some examples of super-resolution include example-based superresolution [1] and super-resolution using sparse coding [2]In addition, there have been attempts at super-resolution
by processing the background and foreground in a single
image [3] and methods doing super-resolution by distributing the luminance value of the local region to character
images [4]. Furthermore, in recent years, a highly accurate
super-resolution method using a neural network has been
proposed, called a Super-Resolution Convolutional Neural
Network (SRCNN) [5], [6]. SRCNN is a super-resolution
method which uses a Convolutional Neural Network (CNN)architecture. In addition, methods have expanded SRCNNs
with the use of additional layers [7]–[9]. However, most
methods do not specialize for specific classes and mostaccuracy improvements have been used for natural scene
images.
By narrowing the target to images with characters or text,
it might be possible to realize super-resolution with higher
accuracy for scene text images. Therefore, we experimentwith SRCNN trained using only images with text andFigure 1.

The combination of CNF with SRCNN.

compare it to an SRCNN trained for general object superresolution. From this comparison, we are able to analyze
the characteristics of the SRCNNs with the different training
regimens.
In addition, we can attempt super-resolution for images
containing both text and objects through the use of Contextwise Network Fusion (CNF) [10]. CNF integrates the outputs of individual CNNs in additional layers with finetuning in order to fuse heterogeneous information. Shownin Fig. 1, by fusing an SRCNN trained for super-resolution
with objects and an SRCNN trained for super-resolution
with text, we show and verify the accuracy improvementof images with natural scene text.
The contribution of this paper is twofold. First, weanalyze the differences in characteristics with SRCNNs
trained with object images and text images. Specifically,
we use quantitative and qualitative evaluations of the superresolution capabilities of the SRCNNs. Furthermore, wefind the correlation between layers using Singular Vector
Canonical Correlation Analysis (SVCCA) [11] and compare
the filters of each SRCNN using t-Distributed Stochastic
Neighbor Embedding (t-SNE) [12]. Second, we propose theuse of CNF to combine SRCNNs in order to produce a morerobust model.
II. R ELATED W ORK
General image super-resolution is an active field in machine learning. There are many models based on CNNs,
such as SRCNNs [5], [8], [9], Rapid and Accurate Image Super-resolution (RAISR) [13], Residual Channel At-

tention Networks (RCAN) [14], Deeply-Recursive Convolutional Networks (DRCN) [15], and Very Deep SuperResolution (VDSR) [7].
The super-resolution of images with text is also a growing
field. Most super-resolution models involving text use text
derived from documents. For example, Capel and Zisserman [16] used a maximum likelihood estimator for thesuper-resolution of document-based text images. Banerjee
and Jawahar [17] and Thillou and Mirmehdi [18] use superresolution on document-based characters. There have only
been few works focusing on natural scene text, most ofwhich focus on text from video [19], [20].
III. S UPER -R ESOLUTION C ONVOLUTIONAL N EURALN ETWORKSSRCNNs tackle super-resolution through the use ofimage-to-image CNNs. The input of an SRCNN is a small
low-resolution image and the output is the correspondinglarge high-resolution image. It works by first enlarging the
low-resolution image by Bicubic interpolation then refines
the up-scaled image through three convolutional layers.
A. Total Variation Loss
The SRCNN is trained using Total Variation Loss (TVLoss) in addition to Mean Squared Error (MSE). TV Lossis the difference in luminance values between adjacent pixels
in the entire image. It is useful for text due to being
able to reduce noise and create smoother flat areas while
maintaining sharp boundaries. TV Loss is defined as:
TV(Y) =nm X
X(|yi+1,j − yi,j | + |yi,j+1 − yi,j |),(1)

i=1 j=1

where Y is the super-resolution image which is the outputof CNN, and X is the original image. The total loss L is:
L(Y, X) = MSE(Y, X) + λTV(Y),(2)

where λ is a predefined weight.
B. Character-SRCNN and ImageNet-SRCNN
The standard use of SRCNN for general image superresolution trained using the ImageNet dataset [21]. However, to demonstrate the inadequacy of SRCNN trainedwith ImageNet on images (ImageNet-SRCNN) with text,
we compare SRCNN trained with generated images ofcharacters (Character-SRCNN).
IV. C ONTEXT- WISE N ETWORK F USION WITH SRCNN S
To combine them while maintaining the benefits of each,
we propose using CNF. CNF is a method of fusing CNNs
with extra convolutional layers, in order to combine networks with different characteristics. In [10], CNF is used tomerge SRCNNs with different numbers of layers. However,
we propose using CNF to merge the Character-SRCNN
with the ImageNet-SRCNN to build a unified model. By

combining the networks in this way, the Character-SRCNN
can focus on the text regions and the ImageNet-SRCNN
can focus on the other regions. The proposed model of using
CNF with the two SRCNNs is shown in Fig. 1. In the figure,the fusion layer consists of a convolutional layer with a 3×3
convolution.
In order to train the CNF, the SRCNNs are first trained
for their respective text and general image tasks. Next,
the Character-SRCNN and the ImageNet-SRCNN are fusedusing CNF. Finally, the CNF is fine-tuned using scene text
images in order to take advantage of the two SRCNNs. During the fine-tuning process, the parameters of the SRCNNs
are fixed and only the fusion layers are updated.
V. C OMPARATIVE E VALUATION OF
C HARACTER -SRCNN AND I MAGE N ET-SRCNN
A. Dataset
In order to evaluate super-resolution, we prepared image
datasets with low-resolution and high-resolution counterparts. The low-resolution images were constructed by downsampling the high-resolution images by a factor of 6. Thus,
the SRCNNs aim to enhance images that were magnified by
6.
1) Character dataset: To construct the character dataset,
24,000 serif, sans serif, and script text images were generated. Furthermore, in order to simulate natural scene text, thetext and background colors were randomized and noise wasadded. The size of the characters in the generated images
was randomized. Using multiple character sizes increases
the generalization of the model. Using this dataset, theCharacter-SRCNN is trained to focus on super-resolution of
text.
2) ImageNet dataset: ImageNet [21] consists of real
images such as images of people and scenes. We use about
400,000 from ImageNet selected based on the conditions
outlined in [5], [6]. The ImageNet dataset was used to train
the ImageNet-SRCNN.
3) ICDAR 2013 dataset: For the scene text dataset, weused the International Conference on Document Analysis
and Recognition Robust Reading Competition dataset (ICDAR 2013) dataset [22]. This dataset is used to evaluate the
SRCNNs using images having both natural scenes similar
to the ImageNet dataset and text similar to the Character
dataset. Moreover, this dataset is only used for testing. 4,686
images of text areas were extracted as the test set. The sizeof the characters varies from 8 × 8 to 30 × 30 pixels for each
image.
B. Quantitative Evaluation
In order to quantitatively evaluate the methods, we use
the following four evaluations.

1) PSNR: Peak signal-to-noise ratio (PSNR) is a measure
to calculate the similarity between the ground truth image
and the generated super-resolution image. The evaluation is
performed by determining the ratio between the maximum
luminance M AX and the amount of noise. Namely, the
PSNR is calculated by:
2

P SN R = 10 log10

M AX
.
M SE

(3)

For PSNR, the unit is in decibels (dB) and the larger thevalue the more similar the super-resolution image is to theground truth image.
2) SSIM: Similar to PSNR, Structural Similarity (SSIM)
is a similarity measure between the ground truth and thesuper-resolution image. SSIM is the perceived difference
between two images and it relies on the average luminanceµ, variance σ 2 , and covariance σ for each local region. The
equation for SSIM is:
SSIM =(2µX µY + C1 ) + (2σXY + C2 )2 + σ2 + C ) ,(µ2X + µ2Y + C1 )(σX
2
Y

Table IC OMPARISON OF ACCURACY BETWEEN I MAGE N ET-SRCNN AND
C HARACTER -SRCNN

Character Regions
PSNR (dB) ↑SSIM ↑CCR (%) ↑NIQE ↓
Other Regions
PSNR [dB] ↑SSIM ↑
NIQE ↓

ImageNetSRCNN

CharacterSRCNN

Original
Image

23.9
0.718
55.9
8.77

24.0
0.735
57.2
7.49

∞
1.0
77.1
6.09

29.4
0.891
5.06

29.3
0.883
6.05

∞
1.0
3.82

(a) Low-resolution image(4)

where C1 and C2 are stabilizing constants set to C1 =(0.01 × 255)2 and C2 = (0.03 × 255)2 . SSIM is a value
from 0 to 1 with a larger value meaning more similar.
3) CRR: The Character Recognition Rate (CRR) isthe evaluation of character recognition using the superresolution images. For this purpose, a Convolutional Recurrent Neural Network (CRNN) [23] is used. CRNN is a wellknown method of recognizing text in natural scene images
that uses convolutional layers to extract features, recurrent
layers to predict characters, and a transcription layer using
Connectionist Temporal Classification (CTC). The CRR is
the rate at which CRNN could recognize the text. Larger isbetter and it only applies to character regions.
4) NIQE: Naturalness Image Quality Evaluator(NIQE) [24] is an indicator to evaluate the image
quality. Unlike PSNR and SSIM, this evaluation does not
use the original ground truth images. NIQE evaluates image
quality by comparing features from the evaluated image andstatistical regularities observed in natural images. Smaller
NIQE values are better.

(b) Results of ImageNet-SRCNN(c) Results of Character-SRCNN(d) Accuracy comparison
Figure 2. Comparison of super-resolution on scene text areas. In (d), blue
is where the SSIM value was higher for the Character-SRCNN and red wasfor the Image-SRCNN.

C. Results
Table I shows the results of the quantitative evaluationof Character-SRCNN and ImageNet-SRCNN using the fourmeasures. In the table, the character regions are the areas of
the ICDAR 2013 scene text images which contain charactersand the other regions are the areas that do not. Not surprisingly, the Character-SRCNN shows better results in thecharacter areas and the ImageNet-SRCNN has better resultsin the non-character areas. However, from this result, it canbe said that the SRCNNs can be trained differently to target
specific types of data.

D. Qualitative Analysis
In order to inspect the characteristics of each SRCNN,
we perform pixel specific accuracy comparisons between the
ground truth and the super-resolution images produced byCharacter-SRCNN and ImageNet-SRCNN. Specifically, we
use SSIM to visualize the structure of each image from each
SRCNN. The SSIM is calculated in local regions and theoutput is colored corresponding to a higher SSIM score. Red
local regions are where the ImageNet-SRCNN performed

(a) Ground truth(b) Low-resolution(c) Bicubic interpolation(d) Results of ImageNet- (e) Results of Character- (f) Accuracy comparison
SRCNN
SRCNN
Figure 3. An example of text zoomed in using ImageNet-SRCNN andCharacter-SRCNN.

(a) Low-resolution(b) Results of Character- (c) Accuracy comparison
SRCNN

Figure 4. An example of a character region in which the Character-SRCNN
did poorly.

better and blue local regions are where the CharacterSRCNN performed better.
Comparative results for several images are shown in
Fig. 2. From the figure, it can be seen that the clarity ofthe text is much higher on the images created by CharacterSRCNN. The improvement of the character regions can be
explained by the accuracy comparison in Fig. 2 (d). In theareas surrounding the text, the Character-SRCNN results are
improved, as indicated by blue. Conversely, the backgroundareas are dominantly red pixels in the accuracy comparison.
Fig. 3 shows an example of a zoomed in region. In
this region, the input low-resolution letters have significant
aliasing. While the bicubic interpolation and the resultsfrom the ImageNet-SRCNN have improved anti-aliasing andsmoothing, the results from the Character-SRCNN are sharp
and almost totally reconstructed the ground truth.
While the results for the Character-SRCNN were impressive, there were instances where the super-resolution
suffered. Fig. 4, for instance, shows a zoomed in a pieceof an image where a white aberration effect was added tothe text. In addition, as Fig. 4 (c) shows, excess noise wasadded to the result from the Character-SRCNN. We foundproblems with this generally when the luminance difference
between the text and the background was not significant
enough.
E. Comparison of the Networks
SVCCA [11] is a method to analyze the similarity of
two networks. The inputs of SVCCA are the outputs of

Figure 5.

SVCCA between Character-SRCNN and ImageNet-SRCNN.

each layer and the calculation of SVCCA is divided into
two stages. First, Singular Value Decomposition (SVD) [25]is performed to reduce redundant dimensions which are
not necessary for network representation. Next, CanonicalCorrelation Analysis (CCA) [26] is performed using theresults of SVD. By doing so, we derive the correlation on alayer-by-layer basis.
To compare the Character-SRCNN and ImageNetSRCNN networks, we find the layer-wise correlation between them using SVCCA. Fig. 5 shows the correlation
between the convolutional layers in the Character-SRCNN
and ImageNet-SRCNN. Numerals in each cell indicate correlation coefficients between corresponding layers. From
the figure, there is some degree of correlation between
the corresponding layers. However, they are not exactly
the same, which indicates that each SRCNN has its own
characteristics.
F. Comparison of the Trained Weights
In order to compare the convolutional filters used between
the two SRCNNs, t-SNE is used. t-SNE is a dimension reduction method for high-dimensional data. Using t-SNE, thedimensions can be reduced while maintaining the relation ofhigh-dimensional features. For the comparison between thefilters, t-SNE was performed on the 9 × 9 filters from thefirst layer of both SRCNNs. Fig. 6 shows the visualization. It
can be seen from the figure that the filters of the ImageNetSRCNN are distributed around the edges of the filters of the
Character-SRCNN. Conversely, the filters of the CharacterSRCNN are focused and do not vary as much. Therefore, it
is conceivable that the filters of the Character-SRCNN are
specialized toward certain tasks while the Image-SRCNN
tackles many different features.
VI. E XPERIMENTAL R ESULTS OF SRCNN F USION
A. Quantitative Evaluation
In order to train the CNF using SRCNN networks (CNFSRCNN), we first pre-train the Character-SRCNN and
ImageNet-SRCNN with the respective datasets described in
Section V-A. The second step is to fix the weights of the(a) Ground truth

Figure 6. Visualization of the filters of the first layer of each SRCNN
using t-SNE. The filters enclosed in red are from the ImageNet-SRCNN.
The filters enclosed in blue are from the Character-SRCNN.

PSNR (dB) ↑SSIM ↑
NIQE ↓

ImageNetSRCNN
29.1
0.887
6.25

CharacterSRCNN
28.6
0.882
7.16

(c) Bicubic interpolation(d) Results of ImageNet- (e) Results of Character- (f)SRCNNTable IIC OMPARISON OF ACCURACY BETWEEN EACH SRCNN(b) Low-resolution

SRCNN

Results
SRCNN

of

CNF-

Figure 7.
Comparison of ImageNet-SRCNN, Character-SRCNN, and
CNF-SRCNN in a character region.

CNFSRCNN
29.9
0.897
5.11

SRCNNs and train the convolutional layers in the CNF. The
final step is to test using the ICDAR 2013 scene text dataset.
The results for CNF-SRCNN is shown in Table II. The
CNF-SRCNN performed better in every quantitative evaluation when compared to the SRCNNs separately. This showsthat the CNF-SRCNN is able to use the characteristics ofboth SRCNNs and combine them into one robust superresolution model.
B. Qualitative Analysis
Fig. 7 shows an example of the super-resolution result
by ImageNet-SRCNN, Character-SRCNN, and the proposed
CNF-SRCNN. The text in Fig. 7 is especially small, so thisis a good example to illustrate the differences between themethods. In Fig. 7 (d), there are still aberrations between
the characters, however, in Fig. 7 (e), that noise is fixed.
However, even though the results of CNF-SRCNN have lessnoise, the Character-SRCNN has more pronounced text.
C. Influence of Each SRCNN on CNF
We visualized the influence of Character-SRCNN and
ImageNet-SRCNN on super-resolution images of CNFSRCNN. To visualize the influence, we applied the SSIM
measure for each pixel. Specifically, super-resolution images of CNF-SRCNN, Character-SRCNN, and ImageNetSRCNN are compared.
Fig. 8 shows pieces of images visualizing the influenceon CNF-SRCNN that the two SRCNNs have. Blue indicates that Character-SRCNN is more influential and red
indicates that ImageNet-SRCNN is more influential. From
Fig. 8, ImageNet-SRCNN affects the whole image, whereas
Character-SRCNN influences the CNF-SRCNN near the(a) Low-resolution(b) Accuracy comparison (c) Influence comparison

Figure 8. Influence comparison result of each SRCNN on CNF-SRCNN.
Blue: influence of Character-SRCNN is larger. Red: influence of ImageNetSRCNN is larger.

text. The ImageNet-SRCNN’s influence is most prominent in
the background of the image. However, the influence of the
SRCNNs on the local regions does not necessarily correlatewith accuracy.
D. Using CNF with Other Super-Resolution Methods
The use of CNF for CNN-based super-resolution methods
is not limited to SRCNN. We performed an additional experiment using the proposed method of combining an RCAN
based on ImageNet (ImageNet-RCAN) and one based on
text (Character-SRCNN) using CNF (CNF-RCAN). The
results are shown in Table III. However, the results of
CNF-RCAN are lower than ImageNet-RCAN by itself. The
reason for this is due to the weakness of Character-RCAN.
Unlike SRCNN, RCAN is aggressive at fitting features
to the low-resolution image. Due to this, the CharacterRCAN is over-influenced by the text training method whichcauses the background regions to not be modeled correctly. Thus, using Character-RCAN in CNF is not suited
for scene text super-resolution. Using this knowledge, we
combined ImageNet-RCAN with Character-SRCNN in one

Table III
C OMPARISON OF ACCURACY BETWEEN EACH RCAN

PSNR (dB) ↑SSIM ↑
NIQE ↓

ImageNetRCAN
32.8
0.921
4.87

CharacterRCAN
27.4
0.863
5.53

CNFRCAN
32.5
0.918
5.02

CNFRCAN+SRCNN
32.9
0.921
4.93

model (CNF-RCAN+SRCNN) to get the best of both superresolution methods. Under this scheme, we were able toimprove the results.
VII. C ONCLUSION
In this paper, we realized accurate super-resolution using
SRCNNs for images with text. Specifically, we realized
super-resolution by training an SRCNN using ImageNet
and a generated character dataset separately. Then, we
combined the two SRCNNs using CNF to achieve a higher
accuracy. Through evaluations, we demonstrated that the
CNF-SRCNN was able to produce better super-resolution
results by combining the ImageNet-SRCNN and CharacterSRCNN. Furthermore, we analyzed the differences between
the SRCNNs trained with different datasets and found thatthey have different characteristics. Also, internal analysisof the networks was done using SVCCA for layer-wisecomparison and t-SNE to visualize the differences in thefirst convolutional layer.
ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grant
Number JP17H06100.
R EFERENCES[1] W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Examplebased super-resolution,” IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56–65, 2002.
[2] J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image superresolution via sparse representation,” IEEE Trans. Image
Processing, vol. 19, no. 11, pp. 2861–2873, 2010.
[3] J. Sun, Q. Chen, S. Yan, and L.-F. Cheong, “Selective image
super-resolution,” arXiv preprint arXiv:1010.5610, 2010.
[4] N. Nayef, J. Chazalon, P. Gomez-Kramer, and J.-M. Ogier,“Efficient example-based super-resolution of single text images based selective patch processing,” in IAPR Int. Workshop
Document Analysis Systems, 2014, pp. 227–231.
[5] C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a
deep convolutional network for image super-resolution,” inEuropean Conf. Computer Vision, 2014, pp. 184–199.
[6] C. Dong, C. C. Loy, K. He, and X. Tang, “Image superresolution using deep convolutional networks,” IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp.
295–307, 2016.
[7] J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image superresolution using very deep convolutional networks,” in IEEE
Conf. Computer Vision and Pattern Recognition, 2016, pp.
1646–1654.
[8] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham,
A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al.,“Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint, 2017.

[9] C. Dong, C. C. Loy, and X. Tang, “Accelerating the superresolution convolutional neural network,” in European Conference Computer Vision, 2016, pp. 391–407.
[10] H. Ren, M. El-Khamy, and J. Lee, “Image super resolution
based on fusing multiple convolution neural networks,” inIEEE CVPR Workshops, 2017, pp. 1050–1057.
[11] M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein,“Svcca: Singular vector canonical correlation analysis fordeep learning dynamics and interpretability,” in Advancesin Neural Information Processing Systems, 2017, pp. 6076–
6085.
[12] L. v. d. Maaten and G. Hinton, “Visualizing data using tsne,” J. of machine learning research, vol. 9, no. Nov, pp.
2579–2605, 2008.
[13] Y. Romano, J. Isidoro, and P. Milanfar, “RAISR: Rapid andaccurate image super resolution,” IEEE Trans. Computational
Imaging, vol. 3, no. 1, pp. 110–125, mar 2017.
[14] Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image
super-resolution using very deep residual channel attention
networks,” in European Conf. Computer Vision. Springer,
2018, pp. 294–310.
[15] J. Kim, J. K. Lee, and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” in IEEE Conf.
Computer Vision and Pattern Recognition, 2016.
[16] D. Capel and A. Zisserman, “Super-resolution enhancementof text image sequences,” in Int. Conf. Pattern Recognition.
[17] J. Banerjee and C. Jawahar, “Super-resolution of text images
using edge-directed tangent field,” in IAPR Int. Workshop
Document Analysis Systems. IEEE, 2008.
[18] C. Mancas-Thillou and M. Mirmehdi, “Super-resolution text
using the teager filter,” in Int. Workshop Camera-Based
Document Analysis and Recognition, 2005, pp. 10–16.
[19] H. Li and D. Doermann, “Superresolution-based enhancementof text in digital video,” in Int. Conf. Pattern Recognition,
2000.
[20] K. Donaldson and G. Myers, “Bayesian super-resolution of
text in video with a text-specific bimodal prior,” in IEEE Conf.
Computer Vision and Pattern Recognition, 2005.
[21] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei, “Imagenet: A large-scale hierarchical image database,” inIEEE Conf. Computer Vision and Pattern Recognition, 2009,
pp. 248–255.
[22] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, andL. P. De Las Heras, “Icdar 2013 robust reading competition,”in IAPR Int. Conf. Document Analysis and Recognition, 2013,
pp. 1484–1493.
[23] B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its
application to scene text recognition,” IEEE Trans. PatternAnalysis and Machine Intelligence, vol. 39, no. 11, pp. 2298–
2304, 2017.
[24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Makinga” completely blind” image quality analyzer.” IEEE Signal
Processing Letters, vol. 20, no. 3, pp. 209–212, 2013.
[25] J. C. Nash, “The singular-value decomposition and its use tosolve least-squares problems,” in Compact Numerical Methods for Computers. Routledge, dec 2018, pp. 30–48.
[26] D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonicalcorrelation analysis: An overview with application to learning
methods,” Neural Computation, vol. 16, no. 12, pp. 2639–
2664, dec 2004.