Don’t Miss the Fine Print! An Enhanced Framework to Extract Text
from Low Resolution Images
Pranay Dugar
, Aditya Vikram
, Anirban Chatterjee
, Kunal Banerjee
1 a
and Vijay Agneeswaran
Walmart Global Tech, Bangalore, India
Flipkart, Bangalore, India
Scene Text Recognition, Super-resolution, Text Extraction, Convolution Neural Network.
Scene Text Recognition (STR) enables processing and understanding of the text in the wild. However, road-
blocks like natural degradation, blur, and uneven lighting in the captured images result in poor accuracy during
detection and recognition. Previous approaches have introduced Super-Resolution (SR) as a processing step
between detection and recognition; however, post enhancement, there is a significant drop in the quality of the
reconstructed text in the image. This drop is especially significant in the healthcare domain because any loss
in accuracy can be detrimental. This paper will quantitatively show the drop in quality of the text in an image
from the existing SR techniques across multiple optimization-based and GAN-based models. We propose a
new loss function for training and an improved deep neural network architecture to address these shortcomings
and recover text with sharp boundaries in the SR images. We also show that the Peak Signal-to-Noise Ratio
(PSNR) and the Structural Similarity Index Measure (SSIM) scores are not effective metrics for identifying
the quality of the text in an SR image. Extensive experiments show that our model achieves better accuracy
and visual improvements against state-of-the-art methods in terms of text recognition accuracy. We plan to
add our module on SR in the near future to our already deployed solution for text extraction from product
images for our company.
Textual information contained in images can bolster
the semantic understanding of real-world data. Ex-
tracting text from an image has many applications,
especially in the retail industry, such as, determin-
ing brand name, ingredients, price and country of
origin of a product and detecting profanity. Gener-
ally, this task follows a two-step procedure. First,
localize the text contained in an image using ei-
ther a character-based or a word-based model. Sec-
ond, identify the text in the localized region using a
sequence-to-sequence model. These tasks are chal-
lenging due to the image degradation, image com-
plexities, and diversity in sizes, shapes, and orien-
tations of texts. Recent text extraction models have
performed impressively on clear text but show a sig-
nificant decline in accuracy when recognizing text in
Work done during internship at Walmart Global Tech,
India, when the author was a student at Indian Institute of
low-resolution images (Ye et al., 2020; Feng et al.,
2019; Baek et al., 2019).
Over the years, various deep learning models have
been designed to improve the quality of the images,
and the items present in these images based on the use
cases. Super-Resolution (SR) is one such technique
used to improve the quality of an image by increasing
its resolution while retaining edge consistency, creat-
ing a High-Resolution (HR) image from its Low Res-
olution (LR) counterpart. Various SR methods have
been suggested based on deep neural architectures
which show great promise. However, on attempting
to utilize these models on the task of text extraction,
it was observed that the image lost the clarity of text
even though the overall image became sharper than
the original.
In this paper, we attempt to address some of these
problems. The significant contributions of our work
An approach to generate synthetic LR-HR paired
data that is generalizable to real case scenarios for
product images.
Dugar, P., Vikram, A., Chatterjee, A., Banerjee, K. and Agneeswaran, V.
Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images.
DOI: 10.5220/0010971100003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
A variation of perceptual loss termed recogni-
tion loss that effectively deblurs and sharpens the
boundaries of the texts in the image while preserv-
ing textual characteristics.
An improvised multi-loss function composed of
detection and recognition losses as well as image
Qualitative and quantitative view of how PSNR
and SSIM (Hor
e and Ziou, 2010) are not good
measures of image quality post super-resolution
for textual details.
Visually and analytically superior results for
text super-resolution as compared to existing ap-
It is worth noting that we plan to add our super-
resolution solution shortly into our current deploy-
ment for text extraction from product images (Dugar
et al., 2021), which has been in production for a year
within Walmart.
The paper is organized as follows. In Section 2,
we cover related work along with our motivation.
Section 3 presents our methodology. The experimen-
tal results can be found in Section 4. Some of the ad-
ditional application areas of our method are described
in Section 5. The paper is concluded in Section 6.
Text extraction from scene images is a widely stud-
ied topic. Many accurate and efficient methods that
extract textual information from scene images have
been proven effective in different constrained scenar-
ios. The focus of many of the recent works (Wei Liu
and Han, 2016; Liu et al., 2018; Luo et al., 2019)
has been on natural scenes, which address challenges
due to the high diversity of texts in blur, orientation,
shape, and low-resolution. Traditionally, the problem
to extract text from a low-resolution image is thought
to have two primary aspects: super-resolution and text
Super-resolution aims to output a high-resolution
image that exhibits consistency with the correspond-
ing low-resolution image. Traditional approaches,
such as bilinear, bicubic or designed filtering, are
based on the assumption that the neighbouring pixels
exhibit similar colours and produce the output by in-
terpolating colours between neighbouring pixels. In
the deep learning era, one of the most common ap-
proaches to address this problem is to map it to a re-
gression problem, where we design a complex non-
linear function that outputs the high-resolution im-
age on being fed the low-resolution image as an in-
put (Dong et al., 2016; Kim et al., 2016; Ledig et al.,
2017). Then the textual information is extracted from
the high-resolution image.
As far as the text recognition is concerned, there is
literature that adopts a bottom-up fashion (Jaderberg
et al., 2014) that detects individual characters first and
then combines these into a word, or a top-down fash-
ion (Jaderberg et al., 2015a) that treats the word im-
age region as a whole and addresses it as a multi-
class image classification problem. Based on the fact
that the scene texts generally appear in character se-
quences in scene text images, CRNN (Shi et al., 2017)
maps it to a sequence recognition problem and lever-
ages the Recurrent Neural Network (RNN) to model
the sequential features. Recently, attention mecha-
nism has gained importance in text recognition liter-
ature (Luo et al., 2019). ASTER (Shi et al., 2019)
addresses the problem with oriented or curved texts
using Spatial Transformer Network (STN) (Jaderberg
et al., 2015b), which is followed by text recognition
using an attentional sequence-to-sequence model.
However, the main difficulty of recognising LR
text is that the optical degradation blurs the charac-
ters’ shape, which impedes the methods mentioned
above to exhibit optimal performance while extract-
ing text from many low-resolution images. In this
work, we experiment with different kinds of loss func-
tions, such as a variant of the perceptual loss, and an
improvised multi-loss function combining both detec-
tion and recognition losses to get over the problem of
blurring of character shapes.
3.1 Data Collection and Annotation
The efficacy of the neural networks to approximate
any function depends heavily on the dataset used to
train the model. Previous approaches have generated
a paired LR-HR dataset by downsampling the HR im-
ages using the existing interpolation methods such as
linear, bicubic, and nearest-neighbour interpolation.
However, we cannot take such a dataset as a sam-
ple representative of the natural scene text datasets.
A single down-sample formulation generates all the
LR images, and the model only learns the inverse of
the downsampling function to generate the SR im-
ages. Recently, the authors of (Wang et al., 2020; Cai
et al., 2019; Zhang et al., 2019) have suggested us-
ing images taken by a digital camera at different focal
lengths to create an ideal paired LR-HR dataset for
Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images
Figure 1: Architecture diagram representing the complete flow of model training.
image super-resolution. However, this is not a feasi-
ble approach to generate large-scale datasets required
to train models.
We devised an approach to generate a suitable LR-
HR pair for any large dataset to circumvent these chal-
lenges. Our proposal involves a two-stage interpola-
tion method to generate a synthetic dataset that can
mimic the natural scene text datasets. For a paired
2× LR-HR, we first downsample the original im-
age to one-fourth of its original dimensions, followed
by its upsampling to one-half of its original dimen-
sions. Different interpolation techniques were ran-
domly chosen for both downsampling and upsam-
pling to introduce more randomness in the dataset.
We use in-built interpolation methods in the Torchvi-
sion library (i.e. linear, bicubic, nearest, box, Ham-
ming, and Lanczos) for both downsampling and up-
sampling of the images. Further, for training the
model in batch mode, we create image patches of size
400 pixels × 400 pixels of the HR image and 200 pix-
els × 200 pixels for the LR image.
3.2 Loss Functions
Despite high PSNR values, pixel value based loss
functions like Mean Squared Error (MSE), Mean Ab-
solute Error (MAE) fail to generate images with high-
level attributes, such as, textures. However, the exist-
ing perceptual loss function by (Johnson et al., 2016)
uses a pre-trained model to calculate the differences
between the target and the output image in the fea-
ture space of the neural network, and generates high
texture quality images, but fails to do justice with the
reconstruction of the texts in the generated SR image.
Recognition Loss. We add this new loss to the fam-
ily of perceptual losses that focuses entirely on re-
constructing high-quality texts with sharp boundaries
and fine edges in the SR image. We leverage the
feature maps generated by the fourth convolutional
block of the pre-trained encoder of the text recog-
nition ASTER model (Shi et al., 2019). We define
Recognition Loss as the MSE between these feature
maps of the generated SR image and the original HR
image. Note that our experiments have confirmed that
the recognition loss adapts well with various text ex-
traction use-cases, and the reconstructed text is of bet-
ter quality than all other existing techniques.
= ||Ψ
) Ψ
where Ψ is the feature map obtained as an output
of the n-th block of the ASTER’s encoder model.
Through multiple iterations, we found that output of
the 4-th block works the best for text recognition re-
lated purposes.
Gradient Loss. Taking inspiration from
HOG (Dalal and Triggs, 2005), we propose Gradient
Loss to ensure that the model can better detect edges
and corners in the images. The gradient is calculated
along each channel, followed by the mean across
channels to negate abnormalities across different
image channels. Finally, MAE was used to calculate
the gradient loss between the SR and the HR image
= ||I
Here, represents the cumulative gradient of the
image and is calculated as shown below in equation 3.
I =
2 × channels
+ δI
) (3)
where, δ is the gradient of the image along its
height/width, and is calculated as per the (Dalal and
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Triggs, 2005). Please note that δ has the same di-
mensions as the image on which it is calculated, i.e.,
(width, height, channels) while has the dimension
(width, height, 1).
Detection Loss. This loss is proposed to ensure that
the model can detect the precise locations of all the
texts in an image with higher accuracy. A pre-trained
CRAFT model (Baek et al., 2019) was used to gen-
erate the locations of the texts in the SR image gen-
erated by the model and the original HR. We use the
predicted coordinates of the SR and the HR images to
create two mask images consisting of detected regions
being masked out using equation 4 for each pixel. An
MSE across the two masks is taken as the final loss
value. Thus, the loss is a pixel-wise MSE where each
location represents if that pixel is part of text or not.
img mask(p) =
1, if p in detected box
0, otherwise
||HR mask(p) SR mask(p)||
where, P is the total number of pixels in the image,
summation taken over every individual pixel p,
HR mask is the detection mask created for HR image
and SR mask is the detection mask created for SR
Overall loss for the task is defined as:
TotalLoss = λ
+ λ
+ λ
+ λ
+ λ
+ λ
where, λ values are [1e 2, 6e 5, 1e 4, 2e 4,
6e 3, 1e 0] in the same order. Except Total Varia-
tion (TV) Loss, which is measured only on the output
SR image, every other loss functions takes into ac-
count both the HR image and the SR image. L
the perceptual loss calculated on VGG19.
Our high-level architecture diagram is shown in
Figure 1. We start with a HR image from the dataset
that we down-sample using the in-built methods in
Torchvision library to obtain its corresponding LR
image. This LR image is then fed into our super-
resolution model to generate the SR image. The HR
and the SR images are passed as inputs to the detec-
tion model followed by the recognition model. We
collect the losses L
, L
and L
compute their weighted sum termed as L
and use
it to train our super-resolution model.
As in any super-resolution framework, there are two
ways to gauge the performance of a model: visual per-
ception and analytical scores. Through the following
sections, we will cover these two aspects of our model
in detail.
4.1 Dataset
As the focus of our model is to improve the text in an
image, we perform experiments on datasets designed
for the task of text extraction from images. These are
open-source datasets such as ICDAR2013 (Karatzas
et al., 2013), ICDAR2015 (Karatzas et al., 2015) and
SVT (Wang et al., 2011). These three datasets provide
word-level ground truth boxes of text in an image. We
use these ground truth boxes as the area of consid-
eration for our model in terms of visual perception
and the ground truth text for analytical scoring met-
rics. A small caveat, though, is that the ground truth
provided does not comprise all the words in the im-
age but only the significant ones that are more clearly
visible. The design of our model is such that it im-
proves not only these significant words but also the
non-significant words (small/slightly blurred). How-
ever, due to the lack of ground truth, we will see
the improvement for these non-significant words only
through visual perception. We downsample the im-
ages from the three datasets for creating a LR im-
age dataset, and the original images act as the HR
ground truth images. We compare our model against
some state-of-the-art super-resolution models such as
DNCNN (Zhang et al., 2017), IMDN (Hui et al.,
2019) and ESRGAN (Wang et al., 2018).
4.2 Visual Perception
A super-resolution model is only as good as the
amount of finer details that it can improve. The ex-
isting approaches perform effectively in terms of im-
proving the quality of the overall image. However, as
seen in Figure 2, the character boundaries get blurred
after super-resolution in these models. Standard met-
rics used to verify the quality of super-resolution
models are the PSNR and the SSIM scores (Hor
e and
Ziou, 2010). However, as shown in Table 1, these
metrics do not do justice in terms of the quality of the
characters in the image. Some existing models give a
higher value for PSNR and SSIM scores, but the im-
ages tell a different story. Of the six PSNR and SSIM
scores for the three datasets, our model performs best
only for the SSIM score for SVT dataset.
Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images
Figure 2: Super-resolution outputs for a product image with clear text by various models with the given HR reference image
as input. The models names have been specified below the images.
Figure 3: Super-resolution outputs for a product image with small text by various models with the given HR reference image
as input. The models names have been specified below the images.
The quality of the text drops even further while
considering the words that are not significant. This
drop can be seen clearly in Figure 3. Though not en-
tirely accurate, our model gives much better character
boundaries than the existing models. Since visually it
is clear that the model is performing significantly bet-
ter and that PSNR and SSIM scores are not effective
measures, we performed a more rigorous analysis to
show that the model is significantly better in terms of
text recognition.
To reduce the chance of misinterpretation, we
had asked three annotators to independently check
the images produced by the competition (ESR-
GAN (Wang et al., 2018), IMDN (Hui et al., 2019),
DNCNN (Zhang et al., 2017)) and ours to identify the
one from which understanding the text was the easiest
– this was a blind process, i.e., the annotators did not
know which method produced which output. For this
experiment, we had chosen 20 images from each of
the datasets: ICDAR2013, ICDAR2015 and SVT. We
found that in 75% of the cases, the images produced
by our method was declared the winner in spite of
having lower SSIM and PSNR scores, as mentioned
in Table 1. Kindly, note that the images in Figure 2
and Figure 3 are sample images which depict these
4.3 Text Recognition Analysis
From the SR images generated from different mod-
els, using the ground truth boxes provided in the
dataset, the text areas are cropped and sent through
the text recognition model defined in (Dugar et al.,
2021). First, we compare the accuracy a direct
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 1: PSNR and SSIM scores of various models compared against our model. Note that the scores are averaged over only
the regions of the ground truth boxes used in text recognition as these represent our areas of concern.
ESRGAN 29.432 0.827 29.338 0.826 30.458 0.839
IMDN 32.266 0.881 32.170 0.881 33.383 0.895
DNCNN 32.022 0.897 32.017 0.897 32.464 0.910
Our Model 29.236 0.882 29.122 0.881 32.545 0.928
Table 2: Normalised Edit Distance (Norm ED) of text and accuracy of an exact match for images generated from the two
models (our model: Text SR Image and generic model: IMDN SR Image); we also provide these scores for the High-
Resolution (HR) image for reference. Note that we use the same text recognition model in all three cases.
Dataset Score Type
HR Image Text SR Image IMDN SR Image
ICDAR2013 Norm ED 0.954 0.928 0.919
Accuracy 0.903 0.876 0.833
ICDAR2015 Norm ED 0.972 0.958 0.938
Accuracy 0.908 0.890 0.836
SVT Norm ED 0.930 0.921 0.848
Accuracy 0.827 0.821 0.721
match of ground truth word, and normalised edit dis-
tance (Marzal and Vidal, 1993) a character level
comparison, of the backbone IMDN model against
the model trained by our approach. For reference,
these were both compared against the accuracy score
on HR images, and we present the results in Table 2.
The model trained by our approach gets closer to the
accuracy score for the HR images.
The results motivated us to compare our model
against other state-of-the-art super-resolution models.
Table 3 shows the performance of various models on
the given datasets. On all the datasets, our model per-
forms significantly better than these models in terms
of text recognition.
Though the PSNR and the SSIM scores of our
model are lower than that of the existing models, it
still achieves a better result in both visual and analyt-
ical terms.
The technology described here is generic enough to
be applied to various other application areas beyond
what we report on product images here and in (Dugar
et al., 2021) albeit with some domain-specific finetun-
ing. We note a couple of such application areas here.
5.1 Healthcare
Walmart is devoted to serving its customers by deliv-
ering goods and merchandise at affordable prices and
Figure 4: Extracting manufacturing and expiry dates from a
medicine bottle. Note that the font, size and color of these
dates are blurrier from the rest of the label, and also not
by facilitating healthier lifestyles. In addition to sell-
ing medicines at our stores, Walmart Health (Staff,
2019a) now provides primary, urgent and preven-
tive healthcare services in some of our supercenters.
While selling or administering medicine, one must be
very careful about its expiry date to prevent harmful
effects. Moreover, getting the ingredients wrong for a
medicine may also endanger human lives. Therefore,
unlike standard product images, the tolerance level of
making a false prediction is close to zero in health-
care. Extracting the dates, especially, can be much
more challenging because these are added to the la-
bels at a later stage and are often more obscure than
the rest of the text; an example of the same can be
found in Figure 4. Our solution can be helpful in
this domain with some small improvements, such as,
adding the names of the drugs and their constituents
Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images
Table 3: Accuracy and Normalised Edit Distance for text recognition from images generated by our model against images
from other state-of-the-art super-resolution models.
Accuracy NormED Accuracy NormED Accuracy NormED
ESRGAN 0.808 0.881 0.814 0.905 0.684 0.817
IMDN 0.833 0.919 0.836 0.938 0.721 0.848
DNCNN 0.853 0.919 0.863 0.945 0.726 0.853
Our Model 0.876 0.928 0.890 0.958 0.821 0.921
into our dictionary because these names do not appear
in regular text.
5.2 Edge Devices
Figure 5: Extracting information about products on display.
This information may help in identifying low or out-of-
stock products, and/or notifying damaged products.
Recently, Walmart has given away smartphones
with built-in apps to 740K associates to help them
in their day to day activities in various ways (Staff,
2021b). We can further leverage these devices for in-
ventory management and quality checks; for exam-
ple, an associate may take a picture and notify the
warehouse administration upon detecting a damaged
product. However, the cameras mounted on the smart-
phones may not be of high definition, or the pictures
may be taken from a distance, or there can be jerky
hand movements – all of which may lead to low qual-
ity, tiny or blurry images. Similarly, the surveillance
cameras placed on top of the aisles in Walmart stores
and clubs may also be re-purposed to additionally
gather information on products that are low or out-
of-stock and identify damaged goods (Staff, 2019b);
however, these images may again be of low quality.
Our SR based solution may also contribute in such
cases as shown in Figure 5. Another potential use case
can be reading road signs for autonomous cars; Wal-
mart has been looking in this space for supply chain
management (Staff, 2020), especially for the last mile
delivery (Staff, 2021a).
This paper proves the importance of the scene text
image super-resolution for text detection and recog-
nition. We have proposed an alternative way to gen-
erate the synthetic paired LR-HR dataset that mimics
the actual data compared to the simple bicubic down-
sampling of the HR images. We have demonstrated
that the model trained on our dataset is superior to
the models trained on images generated by bicubic
downsampling to handle scene text images in the wild
through a series of experiments. To handle scene text
image super-resolution, we have proposed Recogni-
tion Loss and an improvised architecture that enables
the model to reconstruct the texts with clear bound-
aries and sharp edges in real-time. Our method out-
performs multiple SR methods by a significant mar-
gin. However, it also shows that we are still far from
decoding the highly degraded low-resolution scene
texts, and the field requires more effort to solve the
In the future, we plan to include more diverse
scene text image datasets across multiple languages
and with different alignments to train the model bet-
ter. We will also try to develop an improved loss func-
tion that will possibly outperform our current bench-
marks. Introducing vision transformers into the scene
text super-resolution domain may further push the
performance, and hence we aim to investigate these
models as well.
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Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images