Component Awareness in Convolutional Neural
Networks
Brian Kenji Iwana∗ , Letao Zhou∗ , Kumiko Tanaka-Ishii† , Seiichi Uchida∗∗ Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan

Email: {brian, shu, uchida}@human.ait.kyushu-u.ac.jp† Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan

Email: kumiko@cl.rcast.u-tokyo.ac.jp

Abstract—In this work, we investigate the ability of Convolutional Neural Networks (CNN) to infer the presence ofcomponents that comprise an image. In recent years, CNNs
have achieved powerful results in classification, detection, andsegmentation. However, these models learn from instance-levelsupervision of the detected object. In this paper, we determine if
CNNs can detect objects using image-level weakly supervisedlabels without localization. To demonstrate that a CNN caninfer awareness of objects, we evaluate a CNN’s classification
ability with a database constructed of Chinese characters with
only character-level labeled components. We show that the
CNN is able to achieve a high accuracy in identifying thepresence of these components without specific knowledge of thecomponent. Furthermore, we verify that the CNN is deducing
the knowledge of the target component by comparing the results
to an experiment with the component removed. This research isimportant for applications with large amounts of data withoutrobust annotation such as Chinese character recognition.

I. I NTRODUCTION
In recent times, artificial neural networks have been successful in many domains and in particular, Convolutional Neural
Networks (CNN) [1] have achieved state-of-the-art resultsin image recognition [2]–[4]. However, these methods tackle
entire image classification. As images become more complex
and computer vision tasks address multiple possible objects,
individual components need to be detected.
Object detection attempts to tackle this by identifying andannotating objects within an image. However, it is a difficult
task because of object variation and interference from thebackground and other objects. Within an image frame, objects
can vary in size, appearance, and location. They can also
suffer from occlusion, complex backgrounds, have multiple
instances, and be overshadowed by other objects. Traditionally,
region-based CNN object detection methods use boundingboxes to isolate region proposals for instance-based classification [5]–[7]. Another approach is to use pixelwise likelyhood
maps for semantic segmentation [8]. However, these methods
require instance-level supervision during training.
Acquiring the large amounts of image data with detailed
object annotations that is required can be difficult and costly toobtain. In this work, we focus on the use of weakly supervisedlabels consisting only entire image-level class annotations ofimages containing multiple objects. The primary contributionof this paper is to investigate the ability of CNNs to learn theFig. 1: Examples of Chinese characters with their components highlighted in color.

awareness of the components with only the inference that itexists somewhere within the context of the image.
Secondly, we demonstrate the use of CNNs to detect thecomponents, or radicals, that constitute Chinese characters.
Chinese characters are logograms that can be deconstructed
into one or more graphical components, shown in Fig. 1.
The components are a combination of strokes that form
the primitives of a character and the recognition of thesecomponents is important for the character’s identification. The
proposed research detects the presence of these components
within Chinese characters with only character-level component
supervision.
In addition, to further verify the CNN’s capability of deducing the structure of the components, we evaluate the model on
images of the Chinese characters with the component removed.
By testing images with the target component removed, we
ensure that the CNN was able learn using inferred knowledgeof the object and not solely rely on surrounding semantic
indicators. This confirms the usability of data lacking priorknowledge of the target’s form for detection tasks.
The remaining of this paper is organized as follows. In
Section II, we briefly summarize the previous work in object detection and Chinese character component recognition.
Section III elaborates on the structure of Chinese characters
and Section IV reviews CNNs and discusses their use. In
Section V, we will expand on the details of our approach andreport the results of the experiment. Section VI demonstrates
the effect that removing the component has on the classification of characters. Finally, we draw a conclusion and describe
future work in Section VII.

II. R ELATED WORKTABLE I: Chinese Compound Character Structures
Description

Example Characters

A. Object Detection
Beyond classification, object detection and segmentation
with convolutional models is an active field. A conventional
approach to tackle object detection use of a CNN with a sliding
window view of the input images [9]. However, this approach
is limited to a constrained class, e.g. faces, and is a brute
force method to address scale invariant object detection andlocalization.
Multi-scale region-based methods isolate regions of interest containing objects within an image using boundingboxes [5], [6]. Regions with CNN features (R-CNN) [7]generates category-independent region proposals for the CNN.
The extracted region proposals are warped and classified independently. Furthermore, the bounding box can be trained using
Support Vector Machines (SVM) [7], multi-task loss withbackpropagation [10], and Region Proposal Networks [11].
Another approach to component identification is the use of
Fully Convolutional Networks (FCN) [8] for end-to-end for
pixelwise prediction and segmentation. To achieve pixel-topixel training, the FCN is provided with full images with anequal sized segmentation ground truth. FCNs have efficiently
achieved state-of-the-art results in semantic segmentation for
natural images [8] and scene text detection [12], [13].
The difference between these methods and the proposed
research is that they require detailed ground truth of the target
objects or their locations. Object detection and localization
using weakly supervised models also has a rich history [14]–[16]. Recently, CNNs have also been used for the localizationof objects with weakly supervised training [17]–[20]. For
instance, Bazzani et al. [21] localizes objects by monitoring a
CNN recognition accuracy when masking regions.
In addition, some word spotting methods use a query-bystring (QbS) approach. This approach searches documents fora target word having only a textual representation. Some ofthese methods include bag-of-features Hidden Markov Models [22] and using Pyramidal Histogram of Characters (PHOC)
as labels enabling QbS [23], [24].
B. Chinese Character Component Detection
In literature, there have been past attempts to detect orextract Chinese character components. Component-based Chinese character recognition is a promising field of online [25]–[27] and offline [28] handwritten Chinese characters due tothe high variation of handwriting. These methods generally
use predefined templates or features to recognize the Chinesecharacter components. Ren et al. [29] uses statistical rules todetermine the layout of Chinese characters in order to tackle
scene text detection. To the best knowledge of the authors,
this is the first trial in using a CNN to detect the presence ofChinese character components.
III. C OMPOSITION OF C HINESE C HARACTERS
Chinese characters are logograms that represent words orpart of words, written within the framework of a square.
The characters can be separated into two categories, basic

Left to Right
Above to Below
Left to Middle to Right
Above to Middle to Below
Full Surround
Surround from Above
Surround from Below
Surround from Left
Surround from Upper Left
Surround from Upper Right
Surround from Lower Left
Overlaidcharacters and compound characters. Compound characters
are characters that combine two or more components within
the bounds of a single character square and they accountfor more than 95% of Chinese characters. For example, theChinese character “明” has two components, “日” and “月.”These components are often semantic indicators, suggesting
the meaning of the character, or phonetic indicator, indicating
the pronunciation of the character. However, in some cases,
the components of modern characters have lost their original
phono-semantic meanings due to obfuscation through simplification, evolution, and stylization [32].
While the structures of most compound characters are easily
identified, there are many instances which can be difficult. For
example, there are some cases where the components intersect,characters without evident regions, and characters comprisingof three or more individual components.
In general, compound characters can be grouped into 12
different structures (see Table I) [33]. The previous example,“明” is Left to Right because it is structured in a way that“日” is on the left and “月” is on the right. In addition,
Chinese characters can contain multiple of these structures.
For example, “昆” is Above to Below with “日” on top and“比” on the bottom. Furthermore, the “比” component can befurther decomposed Left to Right into “上” and “匕.”There are 214 different standardized components, also
known as radicals, defined by the Kangxi dictionary [34].
They range from primitive strokes (e.g. “一” and “丶”) to

時
日
土

寺
寸

Fig. 2: Example of a component tree.

complex structures (e.g. “麻” and “龠”). One difficulty in component classification is the similarity of components. Whiledistinct components, “口” (“mouth”) and “囗” (“enclosure”)
are similar in appearance. Likewise “土” (“earth”) and “士”(“scholar”) as well as “自” (“self”) and “目” (“eye”) are
different components. Another consideration required for identifying components is that they can consist of several separated
strokes such as “氵,” “灬,” and “ 心.” In addition, there isan uneven representation of each component among the total
number of characters. For instance, out of the 6,411 charactersin the database described in Section V-A, only 3 contain thecomponent “龠” and 1,785 contain the component “口.”IV. C ONVOLUTIONAL N EURAL N ETWORKS
CNNs [1] are a class of artificial neural networks which use
a structured connection pattern with shared weights, namely
convolutional kernels, which enforces a rigid sparse connectivity between the neurons maintaining the spatial features andstructures of the data. Recently, they have been widely used inmany recognition tasks due to their record breaking successes
in image recognition [3], [4], [30], [31]. We implement a CNN
to classify characters by the presence of a component.
V. E XPERIMENT AND R ESULTS
A. Dataset
Chinese characters and components provide an ideal sourceof data for the experiment. Unlike natural images, components
of Chinese characters are not always dependent on common
surrounding components. Also, as previously stated, Chinesecharacter components come in different morphologies, scales,
and locations.
Tanaka-Ishii and Godon [35] created a database of 6,411
individual Chinese characters labeled by components.1 In
the database, every character is represented by a component
tree structure with the root node as the entire character andeach subsequent branch as a recursive decomposition of childcomponents. An example of the component decomposition
tree is shown in Fig. 2. Each character in the database wasannotated with up to 10 different component labels.
To determine the component awareness of CNNs, we implemented a one-vs-others evaluation with the target component
compared against examples of Chinese characters without thecomponent. Each component was trained with a 90% - 10%
1 The database and proposed experiment are based in the Japanese versionof Chinese characters.

split between training and test. However, in the database,
there is a large discrepancy between the representation of acomponent and the representation of the other characters. In
one-vs-others training, a heavily unbalanced training set witha low number of the target class would favor the “others.”
To overcome this, characters in the training set that containthe target component were repeated until there was an equal
distribution of the target component and the “others.” It should
be noted that the test set is not augmented.
The dataset used for the CNN experiment contains imagesof the isolated Chinese characters rendered to 56px by 56px
with 50pt black text on a white background.
B. CNN Architecture
For the experiments, a CNN similar to a LeNet [1] isused. The first three layers convolutional layers have 5 × 5
convolutional kernels at stride 1, each with proceeding 2 × 2
stride 2 maxpooling layers. They are made of 32, 64, and 128
nodes respectively. The following two fully-connected layers
have 1024 nodes each and use dropout with a keep probabilityof 0.5. Rectified Linear Units (ReLU) is used for the activation
for all hidden layers. The network is trained in batches of 25
using Adam optimizer [36] with an initial learning rate of
0.001 for 30,000 iterations.
C. Evaluation
In order to evaluate the effectiveness of the CNN, we
calculated the accuracy, precision, and recall for the test set.
Accuracy alone is not robust enough to determine the component awareness due to the previously mentioned unbalanced
representation of the classes in the test set. Thus, precision isused to measure the relevancy of the characters identified tocontain the component and recall is used to determine the ratethat the target component was selected. For the experiment,
precision is defined as:
True Positives
True Positives + False Positives(1)

True Positives.
True Positives + False Negatives(2)

Precision =and recall is
Recall =Overall accuracy is defined as
Accuracy =True Positives + True Negatives.
Total Patterns(3)

D. Results
The results of the evaluation of the model in Table IIdemonstrate that the proposed model is effective in detecting
character components with near perfect accuracy. In general,
for each component, the precision of the CNN was higher than
the recall. This suggests that the CNN has a high accuracy indiscriminating between components but suffers when detecting
the presence of certain components.
Component Shape. Analysis of the results show that the
CNN was able to accurately deduct the shape of the components despite only being trained on the knowledge that theTABLE II: Chinese Character Component Detection Results
Component
亻
艹
灬
扌
氵
二
亠
儿
冂
厂
口
小
山
月
木

Number ofCharacters
363
437
316
235
369
251
579
328
244
374
1,785
556
198
404
813

Accuracy
(%)
99.4
99.4
100.0
99.8
100.0
96.7
96.9
99.4
98.9
98.0
95.8
98.0
98.9
98.8
99.8

Precision
(%)
91.9
100.0
100.0
100.0
100.0
83.3
93.2
93.5
100.0
91.9
99.3
96.6
100.0
97.1
98.9

Recall
(%)
97.1
89.5
100.0
94.7
100.0
51.7
70.7
93.5
70.8
77.3
85.4
83.8
53.3
82.9
100.0

True Positives

False Negatives
Fig. 4: Examples of characters with the component “山” classified by
the CNN. The “山”s are highlighted in blue. When the component
was large proportionally to the character, they were successfullyclassified. When the component was small or had large overlap withother components, they were falsely discriminated as negative.

True Positives
True Positives

True Negatives

False Negative
False Negatives
Fig. 3: Classification results from the test set for the component “冂.”The target component is highlighted in blue. There were no false
positive results.

component exists somewhere within the characters. For example, Fig. 3 shows the evaluated classification results for “冂.”The network was able to identify “冂”s at various locations
and with appearances. Also, despite the true negatives in Fig. 3
having components with similar shapes to “冂,” the CNN
was able to discriminate them from the target component.
However, while the results achieved a perfect precision for“冂,” there were instances of failing to detect the component
which resulted in only a 70.8% recall. The false negatives
reveal that two characters use uncommon shape variations of“冂” and two characters have structures that are Overlaid withthe component “土.”Object size also has an effect on the accuracy of theresults. Unlike many other object detection methods which
use warped images from bounding boxes for classification, thispaper focuses on component awareness in full-sized images.
Thus, components that are too small in size relative to thetraining data can fail to be detected. Figure 4 shows that the
low recall rate of 53.3% for the component “山” was due
to misclassifying characters with small “山”s or instances ofsignificant overlap with other components. Conversely, when
the component was sufficiently large, the CNN was able toderive the shape of the component, even for the conjoinedcases.
Component Context. Some Chinese character components
have strong correlations to the surrounding components.
Shown in Fig. 5, components, such as “亻,” are almost always
accompanied by components on the right. The single false

False Positives

Fig. 5: The false negatives and false positives of “亻” detection as
well as a sample of the true positives. The blue squares indicate
regions which have similar structures, localization, and context as“亻”s, but are actually constructed of three individual components.
“一,” “｜,” and “ノ.”negative, “彳,” is the only instance in the database where the“亻” is isolated from left or right components. Figure 5 also
reveals how similarly shaped components in similar contexts
can lead to incorrectly detecting components. In general,components with context and localization relationships, similarto “亻” (e.g. “艹,” “灬,” “氵,” etc.), had high precision andrecall rates.
Another example where object detection depends on surrounding components is “二.” The difficulty with recalling“二” stems from the difficulty in discriminating between
characters with horizontal lines from “二” and horizontal
lines from other components (Fig. 6). In addition, “二” is acommon sub-component to many other components such as“会,” “矢,” and “雨.” Consequently, the CNN learned to detect“二” contained those particular parent components with a highaccuracy.
VI. A NALYSIS WITH C OMPONENT R EMOVAL
To further support the idea that a CNN can deduce the structure and location of weakly labeled components, the CNNs
were tested on images of characters with the target component

True Positives

False Negatives

Fig. 6: Examples of detection results of “二.”TABLE III: Chinese Character Component Detection Results with
Component Removed
Positives

Negatives

Positive → Negative Example

亻

0

35

→

艹

0

38

→

灬

0

28

→

扌

0

19

→

氵

0

30

→

二

9

20

→

亠

5

53

→

儿

2

29

→

冂

1

23

→

厂

0

44

→

口

2

176

→

小

4

64

→

山

0

15

→

月

1

40

→

木

1

87

→

Component

↓

↓

↓

↓

↓

↓

↓

↓

Fig. 7: Test patterns with the component “月” removed.

removed. If the positive classifications of characters with target
components depend on the existence of those components,
then by removing them, the expected outcome would be only
negative classifications.
A. Data Preparation
To test the models described in Section V-B on Chinesecharacters with the target component removed, a new test
set was created. The test set for the component removal
experiment consists of only characters with the target component. The images were prepared manually by removing only
the relevant component from the previously defined test set.
Figure 7 demonstrates the component removal for “月.”B. Results
Table III confirms the assertion that removing the target
component prevents most object detection by the CNN. This
indicates that the CNN relies on the deduced structures ofcomponents for detection.
With exception to “二,” the component removal process
prevented all but a few characters from being identified as
positive. As stated in Section V-D, “二” faces particulardifficulties in that it is often integrated into other components
as well as existing as a common simple structure in Chinesecharacters. Therefore, a higher ratio of positives reactions weredetected when compared to the other components. Figure 8
reveals the positive detections despite the component being
removed. The unexpected positive results in the figure are
due to instances where the “二” was isolated or was a
sub-component of uncommon components. Alternatively, withexception to the left two negative results, the results showthat removing “二” changed the structure of “会,” “矢,” and“雨” parent components enough to prevent detection. These
two factors affirm the conjecture from Section V-D that when
detecting “二,” the literal structure of “二” is not learned
and rather the presence of parent components is learned. It
is interesting to note that the characters enclosed in red boxes
in Fig. 8 were previously identified as not containing a “二,”but after its removal, the network now detects the presence ofone.
VII. C ONCLUSION
In this work, we demonstrated the ability that weakly
supervised CNNs have on detecting component objects. Utilizing Chinese characters and their components, we showedthat CNNs are able to develop awareness of objects without
prior knowledge of their structures. Using weakly labeled

components without specific annotation allows for the easeof training models for larger datasets.
Our proposed approach achieved a near perfect accuracy in
one-vs-all Chinese character component detection. We showedthat component detection is possible for a variety of components including simple components (e.g. “亻,” “艹,” “二,”“亠,” “冂,” “厂,” and “口”), complex components (e.g. “山,”“月,” and “木”), and disconnected components (e.g. “灬,”“氵,” “二,” “儿,” and “小”). The analysis of the results also
reveal the inferred aspects of components that contribute toawareness, such as shape, size, and location.
To further verify that CNNs grounded detection on the components rather the surroundings, we conducted an experimenttesting the Chinese characters with the components removed.
The results showed that by removing the components, CNNs
have difficulty detecting them.
Future research will be done in moving from single-label
classification to multi-label classification. Chinese charactershave one or more components, thus it is important to de-Positives

Negatives
Fig. 8: Detection result examples with the component “二” removed.
The red strokes indicate regions that were removed prior to testing.
Due to the component removal, negative results are expected and positive results are erroneous. The enclosed red characters are charactersthat were identified to not contain “二” in the previous experiment.

velop an end-to-end solution to detect all of the components simultaneously. This research leads to future work in
Chinese character recognition using component detection andawareness. By detecting the presence of components thatmake up characters, Chinese character recognition can becompartmentalized into searches for a smaller set of classes
than full-character recognition which requires a very large
number of classes.
ACKNOWLEDGMENTS
This research was partially supported by the Institute ofDecision Science for a Sustainable Society, Kyushu University.
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