Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
(EXTENDED VERSION)

arXiv:1710.10400v1 [] 28 Oct 2017

Chee Kheng Ch’ng
Chee Seng Chan
Centre of Image & Signal Processing, Faculty of Computer Science & Info. Technology,
University of Malaya, Malaysia
chngcheekheng@siswa.um.edu.my, cs.chan@um.edu.my

Abstract—Text in curve orientation, despite being one of the
common text orientations in real world environment, has close
to zero existence in well received scene text datasets such as
ICDAR’13 and MSRA-TD500. The main motivation of TotalText is to fill this gap and facilitate a new research direction for
the scene text community. On top of conventional horizontal
and multi-oriented text, it features curved-oriented text. TotalText is highly diversified in orientations, more than half of
its images have a combination of more than two orientations.
Recently, a new breed of solutions that casted text detection as
a segmentation problem has demonstrated their effectiveness
against multi-oriented text. In order to evaluate its robustness
against curved text, we fine-tuned DeconvNet and benchmark
it on Total-Text. Total-Text with its annotation is available at
https://github.com/cs-chan/Total-Text-Dataset.
Keywords-Scene text dataset;
Segmentation-based text detection

Curve-oriented

text;

I. I NTRODUCTION
Scene text detection is one of the active computer vision
topics due to the growing demands of applications such as
multimedia retrieval, industrial automation, assisting device
for vision-impaired people, etc. Given a natural scene image,
the goal of text detection is to determine the existence of
text, and return the location if it is present.
Well known public datasets such as ICDAR’03, ’11, ’13
[1] (term as ICDARs from here onwards), and MSRATD500 [2] have played a significance role in initiating the
momentum of scene text related research. One similarity
in all the images of ICDARs is that all the texts are in
horizontal orientation [12]. Such observation has inspired
researchers to incorporate horizontal assumption [3]–[7] in
solving the scene text detection problem. In 2012, Yao et
al. [2] introduced a new scene text dataset, namely MSRATD500, that challenged the community with texts arranged
in multiple orientations. The popularity of it in turn defined
the convention of ‘multi-oriented’ texts. However, a closer
look into the MSRA-TD500 dataset revealed that most, if
not all the texts are still arranged in a straight line manner
as to ICDARs (more details in Section III). Curved-oriented
texts(term as curved text from here onwards), despite its
commonness, are missing from the context of study. To the
best of our knowledge, CUTE80 [8] is the only available
scene text dataset to-date with curved text. However, its scale

Figure 1: Annotation details of Total-Text, including transcription, polygon-shaped and rectangular bounding box
vertices, orientations, care and do not care regions, and
binary mask.
is too small with only 80 images and it has very minimal
scene diversity.
Without the motivation of a proper dataset, effort in
solving the curved text detection problem is rarely seen.
This phenomenon brings us to our primary contribution of
this paper: Total-Text, a scene text dataset collected with
curved text in mind, filling the gap in scene text datasets in
terms of text orientations. It has 1,555 scene images, 9,330
annotated words with 3 different text orientations including
horizontal, multi-oriented, and curved text.
Orientation assumption is commonly seen in text detection algorithms. We believe that the heuristic design
to cater different types of text orientations hold back the
generalization of text detecting system against texts in the
real world with unconstrained orientations. Recent works
[9]–[11] have started to cast text detection as a semantic
segmentation problem, and achieved state-of-the-art results
in ICDAR’11, ’13 and MSRA-TD500 datasets. They have
reported successful detection of curved text as well. He et

Figure 2: Curved text is commonly seen in real world scenery.

Figure 3: 1st row: Examples from ICDAR 2013, ICDAR2015 and MSRA-TD500; 2nd row: Slightly curved to extremely
curved text examples from the Total-Text.

(a) Yin et. al. [22] (red bounding box) and Huang et al. [6] (blue bounding box)

(b) Shi et al. [7]

Figure 4: These show that the current state-of-the-art solutions could not detect curved text effectively.

al.[3] system in particular has no orientation assumption
and hueristic grouping mechanism. This bring us to the
secondary contribution of this paper, we looked into this
new solution and revealed how it handle multiple oriented
text in natural scene.

[13] named as the ‘Incidental Scene Text’ (also known as
the ICDAR’15), which is based on 1670 images captured
with wearable devices. It is more challenging than previous
datasets as it has included text with arbitrary orientation and
most of them are out of focus.

II. R ELATED W ORKS

MSRA-TD500 [2] was introduced in 2012 to address the
lack of arbitrary orientated text in scene text datasets. It
has 300 training and 200 testing images; annotated with
minimum area rectangle.

This section will discuss closely related works, specifically scene text datasets and text detection system. For
completeness, readers are recommended to read [12].
A. Scene Text Datasets
ICDARs [1] has three variants. ICDAR’03 started out
with 509 camera taken scene text images. All the scene
texts in the dataset appear in horizontal orientation. In
ICDAR’11, the total number of images were reduced to 484
to eliminate duplication in the previous version. ICDAR’13
further trimmed down the 2011 version to 462 images in
total. Improvement was done to increase its text categories
and tasks. In ICDAR’13, there are 462 images of horizontal
English texts. Recently, ICDAR launched a new challenge

COCO-text [14] was released in the early 2016, and is
the largest scene text dataset to-date with 63,686 images
and 173,589 labeled text regions. This large scale dataset
contains all variety of text orientations: horizontal, arbitrary
and curved. However, it used the axis oriented rectangle as
groundtruth, which seems to be applicable only to horizontal
and vertical texts.
CUTE80 [8] is the only curved text dataset available in
public to the best of our knowledge. It has only 80 images
and limited sceneries.

B. Scene Text Detection:
Scene text detection has seen significant progress after
the seminal work by Epshtein et al. [15] and Neumann and
Matas [16]. In the former, Stroke Width Transform (SWT)
was proposed to detect text. This method considered similar
stroke widths to group text components and studied the component properties to classify them. In the latter, Maximally
Stable Extremal Regions (MSER) was exploited to extract
text components. They used geometrical properties of the
components and a classifier to detect text. Both represent
character better than all other feature extractors like color,
edge, texture and etc. Upon picking up potential character
candidates, these connected components based algorithms
typically go through text line generation, candidates filtering
and segmentation as pointed out by this survey [12].
As to many other computer vision tasks, the incorporation
of Convolutional Neural Network (CNN) in localizing text is
a very active research at the moment. Huang et al. [6] trained
a character classifier to examine components generated by
MSER, with the objective of improving the robustness of
feature extraction process. Alongside this work, [17], [18]
also trained a CNN to classify text components from nontext. This line of work demonstrated the high discriminative
power of CNN as a feature extractor. However, interestingly,
Zhang et al. [9] argued that leveraging on CNN as a
character detector has restricted the CNN’s potential due
to the local nature of characters. Zhang et al. trained two
Fully Convolutional Networks (FCN) [19]: 1) A Text-Block
FCN that considers both local and global contextual info
at the same time to identify text regions in an image, 2)
Character-Centroid FCN to eliminate false text line candidates. However, text line generation, which plays a key role
in grouping characters into a word, did not receive much
benefit from the robust CNN. While most of the algorithms
[9], [18] handcrafted the text line generation process, He
et al. [10] trained a FCN to infer text line candidates. By
cascading a text region and a text line using supervised FCN,
Cascaded Convolution Text Network (CCTN) achieved generalization in terms of text orientations, and is one of the best
performing system in both horizontal and abritrary oriented
scene text datasets: ICDAR 2013 and MSRA-TD500.
III. T OTAL -T EXT DATASET
This section will discuss a) the motivation of collecting Total-Text; b) observation made on horizontal, multioriented, and curved text; c) orientation assumption aspect
in the current state-of-the-art algorithms, and d) different
aspects and statistics of Total-Text.
A. Dataset Attributes
Curved text is an overlooked problem. The effort of
collecting this dataset is motivated by the missing of curved
text in existing scene text datasets. Curved text can be easily
found in real life scenes such as: business logos, signs,

entrances etc as depicted in Fig. 7d, surprisingly such data
has close to zero existence in the current datasets [1], [2],
[13]. The most popular scene text dataset over the decade,
ICDARs have only horizontal text [12]. Consequently, vast
majority of algorithms assume text linearity to tackle the
problem effectively. As a result of overwhelming attention,
performances of text detections in ICDARs are saturated
at quite a high point (0.9 in terms of f-score). Meanwhile,
multi-oriented text also received a certain amount of attention from this community. MSRA-TD500 is a well known
dataset that introduced this challenge to the field. Algorithms
like [9], [20] were designed to cater multi-oriented text. To
the best of our knowledge, scene text detection algorithms
designed for curved orientation [8] in consideration is relatively unpopular. We believe that the lack of such dataset is
the obvious reason why the community has overlooked it.
Hence, we propose Total-Text with 4,265 curved text out of
9,330 total text instances, hoping to spur an interest in the
community to address curved text.
Curved text observation. Geometrically speaking, a
straight line has no angle variation along the line, and
thus can be described as a linear function, y = mx + c.
A curved line is not a straight line. It is free of angle
variation restriction throughout the line. Shifting to the scene
text perspective, we observed that horizontal oriented text
or word is a series of characters that can be connected
by a straight line; their bottom alignment in particular for
most cases. At the same time, multi-oriented text, in scene
text convention, can also be connected by a straight line,
given an offset with respect to a horizontal line. Meanwhile,
characters a in curved word will not have unified angle
offset, in which deemed to fit a polynomial line in text
level (refer to Fig. 3 for image examples). In our dataset
collection, we found out that curved text in natural images
could vary from slightly curved to extremely curved. Also,
it is not surprising to find that most of them are in the shape
of a symmetric arc due to the symmetrical preferences in
human vision [21].
Orientation assumption. We observed that orientation
assumption is a must in a lot of algorithms [3]–[6], [9],
[20]. We took a closer look into the orientation assumption
aspect of existing text detection algorithms and see how
it fits into the observation we have made on the curved
text. We mainly focused on systems in which the authors
claimed to have multi-oriented text detection capability and
reported their results on MSRA-TD500. Zhang et al. [9]
first used the FCN to create a saliency map and generate
text blocks. Consequently, the system draw a straight line
from the middle point of the generated text blocks, aiming
to hit as many character components as possible; the straight
line with the angle offset that hit the most text blocks will be
considered as text line for the subsequent step. We believe
that such mechanism would not work in our dataset, as a
straight line would miss the polynomial nature of curved

Figure 5: Comparison between conventional rectangular bounding box (red colour) and the proposed polygon-shaped
bounding region (green colour) in Total-Text. Polygon-shaped appeared to be the better candidate for groundtruth.

(a) Various text orientations (from left to right).
Top (One orientation): HC; VC; Cir and W.
Middle (Two orientations): Cir+H; MO+HC; W+H.
Bottom (Three orientations): H+MO+VC; H+MO+HC; H+MO+Cir

(b) Various text fonts and image backgrounds

Figure 6: Total-Text dataset is challenging due to its highly diversified orientation compositions and scenery.
Legends: H=horizontal, MO=multi-oriented, HC=horizontal curve, VC=vertical curve, Cir=circular and W=Wavy.
text. [20] focused on the text candidate construction part to
detect multi-oriented text. Their algorithm will first clusters
character pairs with consistent orientation or perspective
view into the same group. As we can see in Fig. 3 (second
row, second and third image specifically), characters in a
single curved word could have multiple variations in terms of
orientation. In fact, both of these algorithms, along with [7],
have reported their failure on the same curved text images
in MSRA-TD500 as illustrated in Fig. 4b. It is worth to note
that MSRA-TD500 has only 2 curved text instances in the
entire dataset. Last but not least, we ran [22] and [6] on
several images of Total-Text, results can be seen in Fig. 4.
Focused scene text as a start. Two of the latest scene text
datasets, COCO-text and ICDAR 2015 emerged to challenge
current algorithms with incidental images. For example,
scene images in the ICDAR 2015 [13] were captured without
prior effort in positioning the text in it. Although it was
not mentioned explicitly, one can deduce the emergence of
these datasets are possibly due to: i) Performances of various
algorithms on previous ICDARs dataset have saturated at a

rather high point, hence a new dataset with higher level of
complexity is deem required, ii) Well focused scene text are
not likely to be captured by devices in real world scenarios.
While the work done in curved text detection is considerably
rare, we believe that it is at its infant stage. Inspired by
the improvement in scene text detection and recognition
brought by focused scene text datasets, notably ICDARs, and
MSRA-TD500, we believe that focused scene text instead of
incidental scene text is more appropriate to kick start related
research work.
Tighter groundtruth is better. ICDAR 2015 employed
quadrilaterals in its annotation to cater perspective distorted
text [13]. However, COCO-text used rectangular bounding
boxes [14] like ICDAR 2013, which we think is a poor
choice considering the text orientation variations in it. Fig.
15 illustrates the downside of such bounding box annotation.
Text regions cover much of the background which is not
an ideal groundtruth for both evaluation and training. In
Total-Text, we annotated the text region with polygon shapes
that fits tightly, and the groundtruth is provided in polygon

(a) Text instances per image

(b) Text orientations per image

(c) Curve variations

(d) Occurrence of curved text

Figure 7: Statistics of Total-Text dataset

Figure 8: Examples of pixel-level annotation (cropped) in Total-Text.
vertices format.
Evaluation Protocol. Like ICDARs datasets [12], TotalText uses DetEval [23]. We did a modication to the minimum
intersection area calculation stage to handle our polygonshaped groundtruth. The evaluation protocol will be made
available as well.
Annotation Details. Groundtruth in the Total-Text is
annotated in word level granularity. Adopted from the
COCO-text, word level texts are uninterrupted sequence
of characters separated by a space. As mentioned, TotalText uses polygon shapes to bind groundtruth words tightly.
Apart from that, we also included rectangular bounding
box annotation considering most of the current algorithms
generate rectangule bounding box outputs. However, it is
not an accurate representation as a big chunk of background area is included due to the nature of curved text.
Therefore, we do not encourage the usage of rectangular
bounding box in our dataset. Total-Text considers only
English characters in natural images; other languages, digital
watermarks and unreadable texts are labelled as do not care
in the groundtruth. Do not care area picks up by algorithms
should be filtered out before evaluating its performance.
Groundtruth for word recognition is also provided along
with its spatial coordinates. In addition, orientation of every
instances were annotated for modularity convenience. For
example, if one prefer to evaluate curved text detection
ability only, one could leverage this annotation to filter out
intances with other orientations. Last but not least, TotalText also comes with binary mask groundtruth to cater
the recent requirements [9]–[11]. Fig. 1 illustrates all the
aforementioned annotation details apart from the pixel-level

annotation, which is illustrated in Fig. 8. Considering the
scale of this dataset is manageable, authors of this paper
annotated the entire dataset manually and cross checked with
another 3 laboratory members.
B. Dataset Statistics
This subsection will discuss the statistics of Total-Text.
All of the comparisons are made against ICDAR 2013 and
MSRA-TD500, as they are the most common benchmark
for horizontal and multi-oriented focused scene text respectively. Total-Text is split into two groups, training and testing
set with 1255 and 300 images, respectively.
Strength in numbers. Fig. 7 shows a series of statistics
information of the Total-Text. It has a total of 9330 annotated
texts, 6 instances per image in average. More than half
of the images in Total-Text have 2 different orientations
and above, yielding 1.8 orientations per image on average.
Both numbers ranked first against its competitors [12],
showing the complexity of Total-Text. Apart from these
solid numbers, the dataset was also collected with quality
in mind, including scene complexity such as text-like and
low contrast background, different font types and sizes, etc,
image examples in Fig. 6b.
Orientation diversity. Approximate by half of the text
instances are curved, and the other half is split almost
equally between horizontal and multi-oriented. Curve text
has its own variation too. Based on our observation, we
classified them as horizontal curved, vertical curved, circular,
and wavy (refer to 6a for image example). Their composition
in the dataset can be seen in 7c. Although all the images
were collected with curved text in mind, other orientations
still occupy half of the total instances. A closer look into the

Figure 9: Visualization of the activations in deconvolution network. The activation maps from top left to bottom right
correspond to the output maps from lower to higher layers in the deconvolution network. We select the most representative
activation in each layer for effective visualization. (a) Input image; (b) the last 14×14 deconvolutional layer; (c) the 28×28
unpooling layer; (d) the last 28×28 deconvolutional layer; (e) the 56×56 unpooling layer; (f) the last 56×56 deconvolutional
layer; (g) the 112×112 unpooling layer; (h) the last 112×112 deconvolutional layer; (i) the 224×224 unpooling layer and
(j) the last 224×224 deconvolutional layer.
dataset shows that curved text usually appears with either
horizontal or multi-oriented texts. The mixture of orientations in an image, challenges text detection algorithms
to achieve robustness and generalization in terms of text
orientations.
Scene diversity. In comparison to CUTE80 (the only
publicly available curved text dataset), which majority of
the images are football jerseys, Total-Text is much more
diversified. Fig. 7d shows where curved text usually appears. Business related places like restaurant (i.e., Nandos,
Starbucks), company branding logos, and merchant stores
take up of 61.2% of the curved text instances. Tourist spots
such as park (i.e., Beverly Hills in America), museums and
landmarks (i.e., Harajuku in Japan) occupy 21.1%. Fig. 2
illustrates these examples.
IV. S EMANTIC SEGMENTATION FOR TEXT DETECTION
Inspired by the success of FCN in the semantic segmentation problem, [9]–[11] casted text detection as a
segmentation problem, and achieved state-of-the-art results.
While most of the conventional algorithms failed in detecting
curved text, their algorithms have shown successful results
in limited number of examples due to the lack of available
benchmark. The fact that [10] achieved good results without any heuristic grouping rules where most of the other
algorithms need, intrigued us to look into this new breed of
solution. We fine-tuned DeconvNet [24] and evaluated it on
Total-Text, following section will discuss our findings.
A. DeconvNet
We select DeconvNet [24] as our investigation tool due
to two reasons: 1) it achieved state-of-the-art results in semantic segmentation on Pascal VOC dataset and 2) Multiple

deconvolutional layers in the DeconvNet allow us to observe
the deviation finely. The scope of this paper is not proposing
a new solution to solve the curved text problem, hence we
merely convert and fine-tune the network to localize texts.
For complete understanding, readers are encouraged to read
[24].
Conversion. The last convolution layer of the original
DeconvNet has 21 layers for 20 classes in the PASCAL VOC
benchmark [25] and one background class. In this paper,
we reduced it to two layers, representing text and non-text.
Then, we fine-tuned the pre-trained model provided by Noh
et al. [24] with one step training process instead of two
as discussed in the original paper. Apart from these and
the training data, all other training implementations were
consistent with the original paper.
Training Data. Considering the depth of DeconvNet (i.e.,
29 convolutional layers and 252M parameters), we pretrained it using the largest scene text dataset, COCO-text
[14]. Images in the COCO-text were categorized into legible
and illegible text, where we trained our network only on the
legible text as it closely resemble our dataset. Similar to [9],
[10], we first generated the binary mask with 1 indicating
text region and 0 for background. Approximately 15k of
training data were cropped into 256x256 patches to cater
the receptive field of the DeconvNet. Patches with less than
10% text regions were eliminated to prevent overwhelming
amount of non-text data. Roughly 200k and 80k patches of
training and validation data were generated, respectively. We
augmented the data in parallel to the training with horizontal
flipping and random cropping (into 224x224).

Table I: Evaluation of DeconvNet on Total-Text.

Dataset
Total-Text

Recall
0.33

Precision
0.40

F-score
0.36

Figure 10: Successful examples of DeconvNet.

Figure 12: Examples of DeconvNet with lower confidence
at both end of the curved text.

Figure 11: Failure examples of DeconvNet.
B. Experiments
Inference. The inference process was kept to be as simple
as possible. We resized input images to 224×224, then
forward propagated them through the DeconvNet. To generate final detection result, the saliency map was binarized
using a threshold of 0.5, followed by connected component
analysis to group 1s (text) pixels and bound them tightly
with polygons.
Results. The outcomes were evaluated using our evaluation protocol and listed in Table I. As we went through
each of the output saliency maps, we found two consistent
roots that cause such unsatisfactory results: 1) The network
is not robust enough for challenging backgrounds such as
texts attached on repeated patterns such as bricks, gate,
wall, etc.; 2) Multiple word candidates were grouped as one.
Fig. 11 illustrates some failure examples. We suspect the
robustness of the network was affected by its training data.
Such loosely bounded training data with background regions
labelled as ‘text’ could have impacted the training process
to a certain extend. Meanwhile, producing word line level
output is commonly seen in text detection algorithms, we
lack of a segmentation process to separate them into words
level.
Deeper look into the network. As mentioned before,
our primary intention were to investigate the performance
of DeconvNet on text with all sorts of orientations. With no
orientation assumption or any heuristic grouping mechanism
in the design, we managed to find candidates across texts

with all orientations as illustrated in Fig. 10. We were
curious on how and what exactly happened across the
deconvolution network. So, we cropped a specific patch
of an original image that consists of curved text, forward
propagated through the network, and observed the feature
maps in several layers of the deconvolution network. As
we can see in Fig. 9, at the lower layers, we can notice
which part of the feature map is highly activated. As the
layers proceed, finer details emerged, enriching the region
of interest to an extend that we can recognized the characters
in it.
Spatial resolution of feature maps is crucial. Text
detection systems like [9], [10] adopted FCN and skip
connections in their Convolutional Network. Such design
element perserves spatial resolution of feature maps, and in
turn provides better contextual information for their pixelwise prediction task. Similarly, DeconvNet uses a combination of both unpooling layers and learn-able upsampling
convolution filters to infer bigger feature maps layer after
layer. As we can see in Fig. 10, such saliency map is
high in resolution, depicts the actual shape or orientation
of the detected text region. Minimal post-processing steps
are required to retrieve text candidates from it.
Text line supervision is an interesting step forward.
Fig. 12 illustrates several examples where the network is
not confident about the shape of the curved text regions. We
believe that it could be improved with text line supervision
leveraged in [10]. This can be noticed in [10], where the
work showed their results without the FTN, its performance
droped from 0.84 to 0.5 in terms of F-score.
V. C ONCLUSION
This paper introduces a comprehensive scene text dataset,
Total-Text, featuring the missing element in current scene

text datasets - curved text. We believe that curved text should
be included as part of the ‘multi-oriented’ text detection
problem. While it is under research at the moment, we
hope the availability of Total-Text could change the scene.
We fine-tuned and analyzed how DeconvNet responds to
curved text. Spatial resolution of feature maps and contextual
information appeared to be crucial in segmentation based
methods. Such methods are capable of predicting text regions in all sorts of orientations without hard-coded rules.
Inspired by this observation, we plan to explore this area
further with the aim of designing a scene text detect that is
effective against multi-oriented text.
ACKNOWLEDGMENT
This work is partly supported by Postgraduate Research
Grant (PPP) - PG350-2016A, from University of Malaya.
The Titan-X GPU used by this research was donated by
NVIDIA Corporation. We would also like to express our
gratitude towards Jia Huei Tan, Yang Loong Chang and Yuen
Peng Loh for Total-Text image collection and annotation.

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[24] H. Noh, S. Hong, and B. Han, “Learning deconvolution
network for semantic segmentation,” in ICCV, 2015.
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and A. Zisserman, “The pascal visual object classes (voc)
challenge,” International Journal of Computer Vision, vol. 88,
no. 2, pp. 303–338, 2010.

VI. A PPENDIX

Figure 13 illustrates Total-Text dataset has very challenging attributes of real world scenery. For example, perspective
distortion (Fig. 13a); variation in font types (Fig. 13b);
variation in font sizes (Fig. 13c); background with textlike characteristics such as bricks, trees etc. (Fig. 13d);
uneven lighting (Fig. 13e) and low contrast between text
and background (Fig. 13f).

A. Text Orientations
Figure 14 shows examples with multiple text orientations.
From unified orientation in Figure 14a to two orientations
in Figure 14b-14c, to images with all sort of orientations in
Figure 14d.
B. Different Groundtruth Evaluations
Table II and Figure 15 show the comparison between
two different groundtruth in terms of Precison and Recall.
Note that, Green is the detected text using the DeconvNet
algorithm [24], Red is the groundtruth generated using the

(a) Perspective distorted examples.
(b) Different font type examples.

(c) Different font size examples.

(d) Complex background examples.

(e) Uneven lighting examples.
(f) Low contrast examples.

Figure 13: Challenging examples in the Total-Text dataset

(a) Curved-oriented text

(b) Curved and Horizontal-oriented text

(c) Curved and Multi-oriented text

(d) Curved and Horizontal and Multi-oriented text

Figure 14: Different text orientations in the Total-Text dataset
Figure

Matched Ground Truth

15a

’GATE-1’
’BOULANGERLE’
’patisserie’
’PURE’
’ICE-CREAM’
’COSTA’
’COFFEE’
’Astro’
’CLUB’
’INVEN’
’IONS’
’GRANVILLE’
’ISLAND’
’anada’
’JEWELRY’
’MARKET’

15b
15c
15d
15e
15f
15g
15h
15i

Polygon-shaped
Precision
Recall
0.27
1
0.4
0.95
0.93
0.74
0.13
1
0.23
0.99
0.86
0.64
0.5
0.98
0.48
0.98
0.43
1
0.65
0.98
0.88
0.57
0.37
0.98
0.19
0.99
0.86
0.91
0.39
0.99
0.18
1

Rectangle-shaped
Precision
Recall
0.69
1
0.97
0.79
0.96
0.56
0.16
1
0.44
0.98
0.95
0.43
0.91
0.91
0.9
0.93
0.73
0.94
0.89
0.91
0.94
0.45
0.86
0.93
0.25
0.98
0.99
0.84
0.95
0.73
0.48
0.945

Table II: Evaluation results with different groundtruth format. Our proposed polygon-shaped groundtruth, provided alongside
Total-Text bounds text regions tightly and hence provide a more accurate evaluation result.

conventional rectangular box and Blue is the groundtruth
generated using our proposed polygon-shape. Figure 15a15c show examples of the detected text (in green region)
has higher precision score if we choose to employ the
conventional rectangle-shaped groundtruth (red in color).
Figure 15d-15i illustrate several examples of the detected
text (in green region) have lower recall and precision because
it misses a large intersection area with the groundtruth
regions.

C. Groundtruth Examples
Figure 16 and 17 depict the annotation details of TotalText dataset. Every images were annotated into four attributes: 1) spatial locations, 2) transcript, 3) orientation
of each text instances, and 4) binary mask with annotated
region as 1(white), background as 0(black).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 15: Disagreement between polygon-shaped (in blue colour) and rectangle-shaped (in red colour) groundtruth regions.
It is found out that evaluation results using the polygon-shaped groundtruth provides a more accurate representation of
algorithm’s performance.

Figure 16: Examples of Total-Text annotations with polygon-shaped groundtruth. It can be noticed that the polygon-shaped
bounding box tightly bounded the text, the annotations are more comprehensive.

Figure 17: More examples of Total-Text annotations with polygon-shaped groundtruth.

