SIW 2021: ICDAR Competition on Script
Identiﬁcation in the Wild
Abhijit Das1,2, Miguel A. Ferrer3, Aythami Morales4,
Moises Diaz5, Umapada Pal1, Donato Impedovo6, Hongliang Li7, Wentao
Yang7, Kensho Ota8, Tadahito Yao8Le Quang Hung9, Nguyen Quoc Cuong9,
Seungjae Kim10 , and Abdeljalil Gattal11
1Indian Statistical Institute, Kolkata firstname.lastname@example.org
2Thapar University, India
3Univ. de Las Palmas de Gran Canaria, Spain
4Universidad Autonoma de Madrid, Spain email@example.com
5Universidad del Atl´antico Medio, Spain firstname.lastname@example.org
6Universit`a degli Studi di Bari Aldo Moro email@example.com
7South China University of Technology, China
8Canon IT Solutions Inc., Japan
9University of Information Technology, Vietnan
10 NAVER Papago, Korea
11 Larbi Tebessi University, Argelia
Abstract. The paper presents a summary of the 1st Competition on
Script Identiﬁcation in the Wild (SIW 2021) organised in conjunction
with 16th International Conference on Document Analysis and Recogni-
tion (ICDAR 2021). The goal of SIW is to evaluate the limits of script
identiﬁcation approaches through a large scale in the wild database in-
cluding 13 scripts (MDIW-13 dataset) and two diﬀerent scenarios (hand-
written and printed). The competition includes the evaluation over three
diﬀerent tasks depending of the nature of the data used for training and
testing. Nineteen research groups registered for SIW 2021, out of which 6
teams from both academia and industry took part in the ﬁnal round and
submitted a total of 166 algorithms for scoring. Submissions included
a wide variety of deep-learning solutions as well as approaches based
on standard image processing techniques. The performance achieved by
the participants prove the elevate accuracy of deep learning methods in
comparison with traditional statistical approaches. The best approach
obtained classiﬁcation accuracies of 99% in all three tasks with experi-
ments over more than 50K test samples. The results suggest that there
is still room for improvements, specially over handwritten samples and
Keywords: Handwritten and printed script identiﬁcation ·wild ·deep
2 A. Das et al.
Due to the ever-increasing demand for the creation of a digital world, many
Optical Character Recognition (OCR) algorithms have been developed over the
years . Incidentally, script identiﬁcation plays a vital role in OCR pipeline. It
use is also been used for several application such as signature veriﬁcation [2,3],
, scene text detection , . A script can be deﬁned as the graphic form of
the writing system used to write a statement , .
The availability of large numbers of scripts makes the development of a uni-
versal OCR a challenging task. This is because the features needed for character
recognition are usually a function of structural script properties and of the num-
ber of possible classes or characters. The extremely high number of available
scripts makes the task quite daunting and sometimes deterring, and as a result,
most OCR systems are script-dependent . The approach for handling docu-
ments in a multi-script environment is divided into two steps: ﬁrst, the script of
the document, block, line or word is estimated, and secondly, the appropriate
OCR is used. This approach requires a script identiﬁer and a bank of OCRs, at a
rate of one OCR per possible script. Many script identiﬁcation algorithms have
been proposed in the literature. Script identiﬁcation can be conducted either
oﬄine, from scanned documents, or online, if the writing sequence is available
Identiﬁcation can also be classiﬁed either as printed or handwritten, with
the latter being the more challenging. Script identiﬁcation can be performed at
diﬀerent levels: page or document, paragraph, block, line, word, and character.
As it is similar to any classical classiﬁcation problem, the script identiﬁcation
problem is a function of the number of possible classes or scripts to be detected.
Furthermore, any similarity in the structure of scripts represents an added chal-
Hence, to elevate state-of-the-art several benchmarking eﬀort by publishing
publicly available datasets [11, 12] and competition has been organised [13–17].
Consequently, the benchmarking works on script identiﬁcation in the literature
uses diﬀerent datasets with diﬀerent script combinations . Therefore, it is dif-
ﬁcult to carry out a fair comparison of these diﬀerent approaches. Moreover,
the databases employed in related studies usually include two to four scripts. A
few actually include an even higher number of scripts but with not exhaustive
combination with both handwritten and printed samples with diﬀerent level of
annotation (word, line and document).
Hence to alleviate this drawback, in this competition we aim to oﬀer a
database for script identiﬁcation, which consists of a wide variety of some the
most commonly used scripts, collected from real-life printed and handwritten
documents. The competition is also aim to document the recent development in
this area of research and attract the attention of the researchers. Speciﬁcally, we
aim to answer the following questions:
–How do contemporary script identiﬁcation techniques perform with large
scale challenging document images captured in the wild?
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 3
–What impact do changes in type of data (handwritten and printed) have on
The following contributions that are documented in this report:
–A rigorous evaluation of several contemporary script identiﬁcation approaches.
–A comprehensive analysis of the identiﬁcation approaches.
–A public benchmark with more 80K images from 13 scripts obtained from
real handwritten and printed documents.
2 Benchmarking Dataset
We developed a large dataset for script identiﬁcation tasks, consisting of printed
and handwriting documents of the following 13 scripts: Arabic, Bengali, Gu-
jarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman,
Tamil, Telugu, and Thai . Figure 1 shows an example of words in each script.
The printed documents were collected from local newspapers and magazines,
whereas mostly native volunteers provided the handwritten. All documents were
scanned at 300 dpi of resolution. However, various conditions are included in the
databases, like diﬀerent inks, sheets, font sizes, and styles. As a consequence,
controlled background removal and ink equalization was applied to ensure a
cleaner database. Furthermore, the word segmentation from the documents was
carried out by an automatic system, which was manually ﬁne-tuned later by
checking all individual words.
This novel dataset has not shared at the time of the competition. In addition,
we only consider words extracted from the texts for this competition. Speciﬁ-
cally, the word-based data was divided into Training and Testing sets for this
competition, as summarize Table 1.
–Training. All registered participants in the competition had access to the
training data. The more than 30K training images were divided into two
main subsets with printed (21974) and handwritten (8887) images.
–Testing. Once the deadline was reached, we shared the testing data with
the participants, which consisted of 55814 unlabelled images to identify in
the 13 scripts. The test set includes more than 50K handwritten and printed
images. The type of data (handwritten or printed) as well as the script label,
were not provided to the participant until the end of the competition.
As can be seen, the number of words is diﬀerent in each script. It makes
the script identiﬁcation an unbalance challenge with this benchmarking corpus.
Finally, the images within Training and Testing sets were not the same. They
were randomly extracted from the words available in the MDIW-13 multiscript
document database . In other words, the data in this competition is a subset
of the large database. The database is publicly available12.
4 A. Das et al.
Fig. 1. Example of image-based words used in the competition.
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 5
Table 1. Summary of the word images included in each script for the Training and
Script Training Testing
Printed Handwritten Printed Handwritten
Arabic 1996 570 4206 3370
Bengali 1608 401 949 8919
Gujarati 1229 144 982 37
Gurmukhi 3629 538 5475 135
Devanagari 1706 1203 1076 301
Japanese 1451 352 363 89
Kannada 1183 872 974 1123
Malayalam 2370 575 1950 144
Oriya 1660 333 649 7514
Roman 1574 558 6053 3750
Tamil 451 873 1667 557
Telugu 1261 640 865 161
Thai 1856 1828 1861 2644
Total 21974 8887 27070 28744
3 Evaluation protocol
SIW 2021 was executed in two stages. During the ﬁrst stage participants were
given the training split of the MDIW-13 datset, including the ground truth and
were asked to develop their algorithm. In the second stage, the test split of the
MDIW-13 (without the annotation) was provided to the participants to infer
the script label on the test images.
The detailed tasks for the competition are as follows: 1) Task 1: Script iden-
tiﬁcation in handwritten document; 2) Task 2: Script identiﬁcation in printed
document; and 3) Task 3: Mixed script identiﬁcation: Train and tested with
handwritten and printed.
The evaluation measure used during the competition was the Correct Clas-
siﬁcation Accuracy. This performance measure was calculated as the percent-
age of samples correctly classiﬁed respect the total number of samples available
for each of the tasks. Note that training and test sets present certain class im-
balance and the methods need to deal with this challenge.
Participants performed word level script recognition. The submission which
achieved the best Correct Classiﬁcation Accuracy for Task 3 was considered as
the winner. The ground truth was manually annotated and segmented according
to a semiautomatic process described in .
4 Details of submitted approaches
Six diﬀerent groups submitted their approaches for the ﬁnal evaluation. The par-
ticipants include teams from academia and industry. Table 2 presents a summary
of the participating groups and their approaches. As can be seen, the proposed
6 A. Das et al.
Table 2. Summary of participants and submitted approaches to SIW 2021. The table
lists the abbreviations of the models, as used in the experimental section. PR = Pre-
trained models, EX = External data, HC = Hand-crafted features, AL = Detection and
alignment, EM = Ensemble models, DM = Diﬀerentiate models, Pre = Pre-processing,
post = Post-processing, X= Yes, x = No.
No. Group PR EX HF AL EM DM Pre Post
1 Ambilight X X xXx x X X
2 DLVC-Lab X X x x Xx x x
3 NAVER Papago Xx x x x x Xx
4 UIT MMlab Xx x x X X xX
5 CITS X X x x x x X X
6 Larbi Tebessi x x Xx x x x x
approaches show heterogeneous characteristics: with and without pre-processing
or post-processing techniques, ensemble or unique models, use of augmented
data. We proceed to present a summary of the best systems submitted by each
of the participants.
4.1 The Lab of Ambilight, NetEase Inc. (Ambilight)
This team used semantic segmentation method as our baseline model instead
of classiﬁcation method. The semantic segmentation model is more focused on
the details of every character and can reduce the disturbances of background,
so the semantic segmentation model is better in this task. To fully utilize the
classiﬁcation label, a multi-task training design is introduced to further improve
the performance of the segmentation task. Therefore, a classiﬁcation branch is
added in our proposed framework. Also, to ﬁt text geometric features better,
attention module and deformable convolution are added into the backbone. An-
other highlight of our approach lies in that we use lots of synthetic data and
grid distortion technique to simulate the handwriting style of diﬀerent people,
which are ﬁnally proved valid tricks in this task. During testing phase, we apply
semi-supervised learning technique to ﬁt the test data better. All these strategies
stated above make us achieve the top performance in the competition.
Introduction and Motivation: As stated above the challenges such as
variation in length of texts, ever-changing division, and similar letters even
characters existing in diﬀerent scripts, hence an explicit solution is to design
a framework based on the ﬁne-grained classiﬁcation work. Currently, a popular
model of ﬁne-grained classiﬁcation is mainly based on the attention mechanism,
such as the WS-DAN model (proposed by MARA in 2019), where it uses
the attention mechanism to crop the partial details of the image to assist the
classiﬁcation. However, its attention mechanism is actually a weak supervision
mechanism. For detection-based models, they generally have to use stronger su-
pervision information. For example, for the Part-RCNN, the foreground is
ﬁrst detected, and the detected foreground is scored with a discrimination de-
gree. More detailed supervision information is based on segmentation methods.
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 7
Table 3. Some insight result on diﬀerent architecture by team Ambilight.
Model Printed (mIoU) Handwritten (mIoU)
VGG19 0.9012(Acc) -
ResNet50-FCN 0.9590 0.8327
ResNest50-DeepLab3+ 0.9704 0.864
HRnet48-OCRnet 0.9732 0.8732
HRnet48-OCRnet+DCN 0.9795 0.8958
Swin-Transfromer 0.9654 0.8768
Fine-grained backgrounds are often complex and diﬀerent, but the foregrounds
are very similar. If the foreground can be segmented for classiﬁcation, better
performance will be achieved. Similarly, Mask-CNN is a ﬁne-grained classi-
ﬁcation model based on strongly supervised segmentation information, but due
to the high cost of labelling, it is rarely applied in the industry.
Although only the classiﬁcation information is given in this competition, the
image only ﬁlled with black characters on a white background allows us to calcu-
late the mask required for segmentation directly through the pixel information.
Later, the participants compared diﬀerent encoder and decoder combinations,
tried the classic VGG, Resnet as the encoder, FCN, Deeplab3+ as
the decoder. Also tried the newly proposed Resnest, Swin-Transfromer,
etc.In the end, we combine the current SOTA backbone HRnet and segmen-
tation decoder OCRnet to solve this task. In addition, they also replace part
of the normal convolution in the encoder for DCN convolution to play a role
of weak attention supervision. The following table demonstrates their compara-
tive study on diﬀerent methods and backbones.
Detailed Method Description: (a) Since the training data is black on a
white background, the mask required for segmentation can be obtained according
to the pixel values. After attained the image mask, each pixel has supervision
information, and more local features can be extracted for very similar languages.
Here the participant use HRnet as the encoder to extract image features, and
then use OCRnet as the decoder for the output the segmentation results, and
determine the ﬁnal output category according to the segmentation vote of each
pixel. In addition, they additionally designed an auxiliary classiﬁer for training
to make the model also pay attention to the global features, and used DCN
v2 (Improved Deep Cross Network) convolution instead of 2D convolution to
strengthen the modeling ability of text shapes.
(b) Pseudo-label Fine-tuning : The team use the trained model to
do inference on test set. If more than 70% of the pixels (foreground pixels) are
classiﬁed to be the same language, they assume that the predication is correct.
Samples that meet this condition will be allocated to the training set for ﬁne-
tuning. In this way, iteratively ﬁne-tune the model in multiple rounds.
(c) Synthetic Data and Data Augmentation: For the printed scripts,
they generate millions of synthetic data in diﬀerent scripts and fonts using text
renderer, which can greatly expand the training data. Firstly, they use the syn-
8 A. Das et al.
thetic data to pre-train the model, and then use the given training set for ﬁne-
tuning. For the handwritten scripts, they use grid distortion to simulate hand-
writing changes. This enhancement method can appropriately simulate non-rigid
deformations such as changes in the thickness and length of human strokes to
enhance the robustness of model for diﬀerent handwriting.
(d) Loss Function: The team did a lot of experiments on the loss function,
and ﬁnally used focal loss  to increase the learning weight of the text part.
(e) Augmentation during Testing: They use random-crop-resize during
training, which plays a small-scale multi-scale role. Therefore, during the infer-
ence on the testing set, multi-scale resizing is performed on the intput image.
Results of diﬀerent sizes are blended together to the ﬁnal result.
4.2 South China University of Technology (DLCV-Lab)
The three tasks were treated as classiﬁcation problems and were solved by adopt-
ing deep learning methods. In order to improve the diversity of training data,
the team utilized data augmentation technique  and synthesized a dataset
using fonts in diﬀerent scripts. Finally, they ensemble three CNN-based models,
namely, ResNet-101 , ResNeSt-200  and DenseNet-121  with CBAM
 for better classiﬁcation accuracy.
The ﬁrst question they considered is whether they should adopt machine
learning methods or deep learning methods. In addition to more than 30,000
samples in the MDIW-13 database, they also use the collected fonts to generate
a synthetic dataset. With such a relatively large amount of data for training, they
thought that deep learning methods may outperform machine learning methods.
Among image processing methods in deep learning, Transformer-based models
has received lots of recent attention, while CNN-based models are still the main-
stream. In the absence of the massive training data required by transformer, they
believe CNN-based models may be more suitable. After determining the main
technical route, on one hand, we try various CNN-based models and ensem-
ble the best three models for better classiﬁcation accuracy. On the other hand,
they collect some fonts for synthesizing data, and utilize the data augmentation
technique for text images to improve the diversity of the training data.
Synthesize Data: To synthesize data, fonts in 13 scripts are collected from
the Internet, and corpora are translated from 58,000 English words using the
Google translation API. Then they randomly select fonts and the translated
word corpora to generate 5,000 images for each script, which are added to the
Data Augmentation: The data augmentation technique they used 
embodies diﬀerent transformations for text images, including distortion, stretch
and perspective. In the training phase, there is 50% probability for every sample
to execute each transformation.
Details of Model: After trying a variety of models, the team choose three
separately trained models, Resnet-101, Resnest-200 and Densenet-121 with CBAM
to ensemble. We use the Adam optimizer with a weight decay of 1e-4 and a learn-
ing rate of 1e-4. The learning rate is set manually to 1/10 of its current value
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 9
when the loss value no longer drops. The cross-enropy loss function is adopted
and image resolution of input is set to 300×700. All three models are pre-trained
on ImageNet. It is worth noting that we did not use the synthetic data for Resnet-
101, as it will cause a worse result. Finally, the prediction conﬁdences of the three
models are averaged as the output.
4.3 NAVER Papago (NAVER Papago)
Given the time constraint of the competition, the main strategy and motivation
was to ﬁx the network and conduct quick experiments to conquer the problems
of the data. As many other real world problems do, the data had class imbalance
problem. Also, the images didn’t have rich pixel level information such as color,
contrast, but had much more spatial level information such as shape, font style.
To address these issues, ﬁrst of all, the participant used stratﬁed data sampling
to overcome class imbalance. This helps the model ﬁt to less-frequent-class im-
ages and boosts overall score. Secondly, many spatial level augmentations were
applied to make the model better recognize the newly seen text shapes in test
data. Augmentations such as random shift, scale, stretching, grid distortion were
applied, and in accordance, same augmentations were applied at test time which
also improved score. The network architecture was ﬁxed to ResNet50 for all the
experiments above, and was changed to NFnet-f3 near the end of competition.
In conclusion, the overall approach was to leave most of the settings ﬁxed and
concentrate on one or two most important issues of the competition.
Preprocessing: All images were rescaled, maintaing the width/height ratio,
and padded to have the same size 160x320.
Data split: 10% of each handwritten and printed data were used as vali-
dation set. They were sampled by stratiﬁed sampling, where the ratio of each
class in the sample remains the same as the ratio of each class in the whole data.
Then training data and validation data of both handwritten and printed data
were mixed, for training all at once.
Inference: 1) pseudo-labeling: test predictions that had conﬁdence over 0.99
were pseudo-labeled and used to ﬁnetune model before inference; 2) test time
augmentation: test time augmentation with random scale, random horizontal,
vertical stretching was applied.
4.4 University of Information Technology (UIT MMLab)
Our approach is building a two-stage deep learning system for script identiﬁca-
tion. In the ﬁrst stage, we applied a residual neural network (ResNet)  to
classify the script as handwriting or print. In the second stage, for each type
of these we use our corresponding EﬃcientNet  model to identify the script.
For the best result on private test set, we used EﬃcientNet-B7 for handwritten
script and EﬃcientNet-B4 for printed script.
Handwritten/Printed type classiﬁcation: A Resnet-50 architecture pre-
trained on ImageNet was used as backbone network. We stacked 1 fully connected
layer with 1024 units in front of the output layer. A sigmoid loss function was
10 A. Das et al.
used as binary classiﬁcation. An Adam optimizer with learning rate of 0.0001
was used for the training process. The dataset was splitted with the ratio of 9/1
and then trained in 20 epochs with a batch size of 16. We saw that 20 epochs
are enough for the model to converge.
We trained separately 2 models with regard to 2 diﬀerent random seeds in
train-validation step. It comes to my attention that one of these 2 models helps
to get higher score on printed task. The another model leads to higher score on
handwritten task. So, in the inference phase, the predicting result of 2 models
are compared with each other. The images that make their result diﬀerent will be
considered manually by a visualizing tool. Following these steps, we can create
a quite signiﬁcant handwritten-printed classiﬁcation result.
Script identiﬁcation: For script identiﬁcation phase, we decided to use two
separate EﬃcientNet B4 and B7 models for handwritten and printed scripts. The
backbone network we used is a pretrained network previously trained on a large
ImageNet dataset contains 1000 classes labels, we can take advantage of pre-
trained weights to extract useful image features without retraining from scratch.
With transfer learning approach, we exclude the ﬁnal fully connected (FC) layer
of pretrained model, then we replaced the top layer with custom layers contain-
ing FC layers for identify 13 languages of script allows using EﬃcientNet as a
feature extractor in a transfer learning workﬂow. The features is fed into global
average pooling (GAP) to generate a vector whose dimension is the depth of the
feature, this vector is the input of the next FC Layer. The GAP layer outputs
the mean of each feature map, this drops any remaining spatial information,
which is ﬁne because there was not much spatial information left at that point.
The ﬁnal FC layer 1×13 using softmax activation produces the probability of
each class ranged from 0 to 1.
Because freezing EﬃcientNet and training only custom top layers tends to
underﬁt the training data, training both EﬃcientNet and custom top layers
tends to overﬁt the training data, so the approach is freezing some ﬁrst layers
of EﬃcientNet to make use of the low level features extracted by pretrained
network on ImageNet datasets, then training the remaining layers and top layers.
The team use Adam optimizer with a learning rate of 0.0001 to minimize the
categorical cross entropy loss function. For each type of script (handwritting and
print), They splitted the data into training set and validation set with the ratio
of 8/2. Then, they trained two base models i.e B4, B7, 250 epochs for each model
with a batch size of 16 and only save the best weight with the highest validation
4.5 Canon IT Solutions Inc. (CITS)
Firstly, the participants from this groups made patches by sliding window. Stride
was 56pixels13. Secondly, they classiﬁed each patch using a Eﬃcient Net as
13 56 is 224/4, and 224 is CNN’s default input width
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 11
Lastly the participants calculated sum of conﬁdence of each class of all
patches, and adopted the class corresponding to maximum argument as the
For prepossessing, we used shave 20 pixels(up, bottom, left, right), resize
height to 224 keeping aspect ratio, normalization. The participants used Tesser-
act in post-processing for printed Hindi’s and Gurmukhi’s results.
There were 3 postprocessing steps: 1) trained a CNN model that classiﬁes
images are printed or handwritten; 2) collected images which were classiﬁed
as printed Hindi or printed Gurmukhi; 3) OCR’ed each image in step 2 with
Tesseract of Hindi model and Gurmukhi model. Tesseract outputs conﬁdence
score with OCR result, and we adopted script with higher conﬁdence score.
The participants generated pseudo handwritten images with CycleGAN 
and used as a training dataset. This dataset contained 13,000 images(1,000 im-
ages for each class).
4.6 Larbi Tebessi University (Larbi Tebessi)
Among the diﬀerent methods for research purposes, the texture-based descrip-
tor is preferred by many researchers due to its various advantages, strengths
and beneﬁts. Proposed research method allows to extract highly informative
elements of the printed and handwritten text. Otherwise, they do not involve
comparing pieces of text like-for-like. In this way, the computational cost pro-
vides good insight into the complexity of the system compared to other systems.
However, the proposed research method also has their weak point that must be
considered which is sensitive to noise that makes the extracted features sensitive
to small changes in the handwriting. The textural information is captured using
an oriented Basic Image Feature (oBIF) columns.
In order to increase the performance of the oBIFs descriptor, the participants
combine oBIFs at two diﬀerent scales to produce the oBIF column features by
ignoring the symmetry type ﬂat. The oBIFs column features are generated using
diﬀerent values of the scale parameter σwhile the parameter is ﬁxed to 0.001.
The generated feature vector is ﬁnally normalized.
The oBIFs column histograms are extracted, the both oBIFs column his-
tograms for the scale parameter combination (2,4) and (2,8) are concatenated
together to form the feature vector representing each printed and handwritten
image. Once the features are extracted, classiﬁcation is carried out using Support
Vector Machine (SVM) classiﬁer. We have employed the Radial Basis Function
(RBF) kernel with the kernel parameter selected to 52 while the soft margin
parameter C is ﬁxed to 10. The participants evaluated the oBIF column features
to identify scripts from printed and handwritten images on the dataset of the
ICDAR 2021 competition on Script Identiﬁcation in the Wild (SIW 2021). The
experiment is carried out by using both printed and handwritten samples in the
training and test sets.
12 A. Das et al.
Table 4. Summary of ﬁnal results for each of the three tasks. Correct classiﬁcation
accuracy (ﬁnal rank in brackets). The table presents two baseline methods: 1) the
Dense Multi-Block Linear Binary Pattern  and 2) the Random Chance. T.# Subm
= Total submissions
Group # Subm Task 1 (Handwritten) Task 2 (Printed) Task 3 (Mixed)
Ambilight 16 99.69% (1) 99.99% (1) 99.84% (1)
DLVC-Lab 43 97.80% (3) 99.80% (2) 98.87% (2)
NAVER Papago 26 99.14% (2) 95.06% (5) 97.17% (3)
UIT MMlab 46 95.85% (4) 98.63% (4) 97.09% (4)
CITS 34 90.59% (5) 99.24% (3) 94.79% (5)
Baseline  - 89.78% (-) 95.51% (-) 94.45% (-)
Larbi Tebessi 1 81.21% (6) 86.62% (6) 83.83% (6)
Random - 7.14% (-) 7.43% (-) 7.22% (-)
5 Benchmarking results with analysis and discussion
In this section we proceed to report and analysis the results obtained from
the submission. The Table 4 shows the ﬁnal rank of the competition for the
three diﬀerent tasks. The results are reported in terms of correct classiﬁcation
accuracy. In order to compare the improvement provided by the competition
we released two benchmarking. Firstly, the random chance, which is around
7% for all three tasks. Further, we provided a second benchmark, i.e. the results
obtained using the Dense Multi-Block Linear Binary Pattern as per the reported
We can observe from the Table 4 that the results of the participants ranged
from 83.83% to 99.84%. Most of the submission were based on CNN, and they
outperformed the baseline . As it is expected, the handwritten task represent
a bigger challenge for the participants with accuracy’s lower than in the printed
scenario for most of the scenarios. Further, for the mixed task the accuracy’s
are someway between their printed and handwritten results. Incidentally, the
submission from Ambilight i.e. the winner team had a marginal diﬀerence of
.2% between printed and handwritten task. Also the gap is more less i.e. 0.06%
while considering mixed and printed.The reason behind this expected to be the
approach they chooses while solving this problem. They considered this problem
as a semantic segmenting task and which helps to give this leverage while ﬁnding
the details of the character.
We proceed to further analyse the system submitted by the winning team.
The Figure 2 shows the confusion matrix obtained by the winning approach
(Task 1: Handwritten script identiﬁcation i.e the most challenging task). The
results show that there is room for improvement in some of the scripts, spe-
cially Gujarati, Telugu, Tamil, and Malayalam. Incidentally, Gujarati script were
mostly mis-classiﬁed as Bengali and Oriya, we assume that it is mostly due to the
writes writing style. As a fact Bengali and Oriya script do not have much similar
outlook to Gujarati. While considering Telugu the biggest mis-classiﬁcation was
with Kannada script nearly 8.8%, it can be considered due to the visual simi-
SIW 2021: ICDAR Competition on Script Identiﬁcation in the Wild 13
Ar Ba Gu Gur Hi Jap Kan Ma Or Ro Ta Te Th
Ar 99.97 0 0 0 0 0 0 0 0 0 0 0 0
Ba 0 99.74 0 0.72 0 0 0 0 0.04 0.18 0 0 0
Gu 0 0 100 0 0 0 0 0 0 0 0 0 0.11
Gur 0 0 0 97.83 0 0 0 0 0 0 0 0 0
Hi 0 0 0 0 100 0 0 0 0 0 0 0 0
Ja 0 0 0 0 0 100 0 0 0 0 0 0 0.04
Ka 0 0 0 0 0 0 100 0.70 0 0 0.17 8.87 0.26
Ma 0 0 0 0 0 0 0 98.59 0 0 0.53 0 0.04
Or 0 0.19 0 1.45 0 0 0 0 99.96 0.24 0.53 0 0
Ro 0.03 0.03 0 0 0 0 0 0 0 99.55 0.17 0 0.07
Ta 0 0 0 0 0 0 0 0 0 0 98.24 0 0
Te 00.03 0 0 0 0 0 0.70 00.03 0.17 91.12 0.04
Th 0 0 0 0 0 0 0 0 0 0 0.17 099.43
Fig. 2. Confusion Matrix (Task 1) obtained by the best method (Ambilight).
larity of the two script. This was the highest mis-classiﬁcation and as a reason
Telugu script classiﬁcation attend the lowest performance. Although it is inter-
esting to ﬁnd that Kannada script had perfect classiﬁcation. Similar to Kannada
and Telugu, Malayalam and Tamil script have high visual similarity, consider-
ing Tamil the highest mis-classiﬁcation was with Malayalam script, but which
is not the case while considering Malayalam highest mis-classiﬁcation was found
between Kannada and Telugu. Hence, we cannot conclude that visual pattern
and structure only responsible while considering script identiﬁcation multi-script
Summarising the competition, from Fig. 3 we can conclude that the maxi-
mum correct classiﬁcation accuracy obtained by all the participants during the
14 days is nearly 100%. The results obtained during the ﬁrst week of the com-
petition represent a period where participants adapted their systems to the test
set. We can see a performance improvement from 91% to 97.4% during this
ﬁrst week. The second week shows a constant improvement with a ﬁnal correct
classiﬁcation accuracy of 99.84%. In a competition with two weeks available for
submissions, we cannot discard certain overﬁtting to the test set. However, the
large number of samples (more than 50,000) and the diﬀerent characteristics of
the tasks (handwritten vs printed) is a added challenge.
The 1st edition of the Script Identiﬁcation in the Wild Competition was organ-
ised to evaluate and benchmark the performance of contemporary script identi-
ﬁcation techniques captured in the wild and explore the robustness of existing
models w.r.t. to changes in the font, size, ink and printing quality used for doc-
ument development and image acquisition as well as changes in the external
acquisition conditions. A total of 6 groups participated in the competition and
contributed 6 algorithm for the group evaluation. The identiﬁcation algorithms
14 A. Das et al.
# Day of the competition
12345678910 11 12 13 14
Fig. 3. Maximum correct classiﬁcation accuracy (Task 3) obtained by all the partici-
pants in each day of the competition.
were compared in terms of printed, handwritten and mixed documents. Most of
the submitted models ensured solid identiﬁcation results in most experimental
scenarios. It is worth mentioning that for some the combination of script the
performance was slightly lower which requires further attention.
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