







































 

 P-ISSN : 2715-2448 | E-ISSSN : 2715-7199  

Vol.3 No.2 July 2022  

Buana Information Tchnology and Computer Sciences (BIT and CS) 

 

53 | Vol.3 No.2, July 2022 

 

Changing Data Image Into Numeric Data on Kiln Manufacture Machinery Use 

Optical Character Recognition (OCR) 

 

Agustia Hananto1  

Study Program 

Information System 

Universitas Buana Perjuangan Karawang 

agustia.hananto@ubpkarawang.ac.id 

Elfina Novalia 2 

Study Program 

Information System 

Universitas Buana Perjuangan Karawang 

elfinanovalia@ubpkarawang.ac.id 

 ‹β› 
 

Goenawan Brotosaputro3 

Study Program 

Master in Computer Science 

Universitas Budi Luhur 

goenawan.brotosaputro@budiluhur.ac.id

Abstrak— Pada proses pembuatan genteng keramik terdapat 

proses pembakaran menggunakan mesin kiln manufacture atau 

oven. Untuk memastikan proses pembakaran berjalan dengan 

baik, dilakukan pemantauan 39 parameter yang ada pada mesin 

kiln tersebut yang harus di awasi secara manual berdasar data 

yang dihasilkan citra pada mesin kiln tersebut. Proses 

pemantauan parameter tersebut tidak efektif yang disebabkan 

oleh kesalahan atau kelalaian manusia dan sifat manusia lainnya 

yang mengakibatkan kerugian bagi perusahaan. Oleh karena itu, 

dibutuhkan sistem untuk menyimpan data parameter mesin 

pembakaran genteng keramik yang dapat disimpan kedalam 

sebuah data base. Setelah dilakukan analisis pada perangkat 

pembakaran (kiln) tersebut, recorder sensor yang menampilkan 

data parameter dapat diakses melalui jaringan LAN (Local Area 

Network), akan tetapi data yang dihasilkan dalam bentuk citra 

bukan dalam bentuk data digital alfanumerik. Data citra yang 

didapat perlu diterjemahkan menjadi data alfanumerik sebagai 

sumber data. Melalui pengenalan optical character recognition 

(OCR) dengan metode template matching, citra tersebut diubah 

menjadi data alfanumerik sehingga dapat di simpan dalam sebuah 

data base. Dari hasil penelitian ini, prototype sistem yang dibuat 

mendapatkan akurasi sebesar 100.00% untuk konversi data citra 

ke data alfanumerik, 

Kata kunci: Citra Digital, Data Numerik Optical Character 

Recognition, Kiln Manufacture. 

Abstract— In the process of making ceramic tiles, there is a 

combustion process using a kiln manufacture or oven. To ensure 

the combustion process goes well, 39 parameters are monitored 

on the kiln engine which must be monitored manually based on 

the data generated by the image on the kiln engine. The process 

of monitoring these parameters is ineffective due to human 

error or negligence and other human traits that result in losses 

for the company. Therefore, a system is needed to store the 

parameter data of the ceramic tile combustion engine which can 

be stored in a database. After analyzing the kiln, the sensor 

recorder that displays parameter data can be accessed via a 

LAN (Local Area Network), but the data generated is in the 

form of an image, not in the form of alphanumeric digital data. 

The image data obtained need to be translated into 

alphanumeric data as a data source. Through the introduction 

of optical character recognition (OCR) with the template 

matching method, the image is converted into alphanumeric 

data so that it can be stored in a database. From the results of 

this study, the prototype system made obtained an accuracy of 

100.00% for the conversion of image data to alphanumeric data, 

Keywords: Digital Image, Optical Character Recognition 

Numerical Data, Kiln Manufacture. 

I. INTRODUCTION  

PT. XYZ is a leading manufacturer of glazed ceramic tile 
(and its accessories). One of the manufacturing processes for 
these products is the combustion process at a temperature of 
1100 degrees Celsius using a kiln manufacture machine or 
oven so that it can produce quality and durable ceramic tiles. 
In the process of burning ceramic tile products, direct 
monitoring is carried out by employees for 24 hours by 
observing 39 data parameters displayed through images on 
the monitor on the kiln engine panel. 

In the process of monitoring the kiln manufacture (oven) 
machine, the sensor data will be displayed in the form of an 
image that appears on a screen that will be updated every 5 
seconds which must be monitored during the process. The 
image displayed on the oven screen will then be recorded 
manually and really must be monitored directly. However, 
human endurance and physical condition greatly affect the 
results of the monitoring. Based on the initial analysis of the 
problem, it can be concluded that the kiln manufacture (oven) 
machine only displays 39 parameter data on a screen in the 
form of an image that will be updated every 5 seconds, the 
data is stored on a small capacity record machine and cannot 
be accessed by data. To overcome this problem, it would be 
possible to create a system or tool that can convert the image 
into alphanumeric data that can provide real-time information 
(monitoring automation). 

Technological developments are increasingly developing 
more advanced than before, as well as image processing 
technology or digital images. Image processing is a method 
of processing images (images / images) into digital form for 
certain purposes. One of the digital image processing studies 
is Optical Character Recognition (OCR) which is a character 
recognition process through preprocessing, segmentation, 
feature extraction and recognition, Optical Character 
Recognition (OCR) is one of the study areas of pattern 
recognition (pattern recognition) in digital images that 
classifying or describing an object based on quantitative 
measurements of its main features or properties. With this 
method is expected to provide a solution to PT. XYZ in 
overcoming the problems that exist in the kiln manufacture 
(oven) machine. 



 

54 | Vol.3 No.2, July 2022 

 

The image can be accessed via a Local Area Network (LAN) 
and through the introduction of optical character recognition 
(OCR) the image can be converted into data in alphanumeric 
form as needed and can be stored into a system that can 
process the data and it is hoped that the application can 
provide information automatically. quickly and precisely to 
the user or user. 

II. METHOD 

2.1 . Study of Literature 

a. Image Processing 
Image processing is the process of processing pixels 
in a digital image for a specific purpose. Initially, 
image processing was carried out to improve image 
quality, but with the development of the computing 
world, which is marked by the increasing capacity 
and speed of computer processing and the 
emergence of computational sciences that allow 
humans to retrieve information from an image[3]. 
The image processing process is a diagrammatic 
process starting from image retrieval, image quality 
improvement, up to a representative statement of 
the imaged image can be seen in the figure 1. 

 

 

 

 

 

Figure 1 Diagram of the digital image processing process. 

 

b. Optical Character Recognition (OCR) 

OCR takes care of the problem of recognizing 

optically processed characters. Optical recognition is 

done offline as well as online. Offline after writing or 

printing is complete whereas online recognition is done 

where the computer recognizes the characters as they are 

drawn. Printed and/or handwritten characters are 

recognizable but the results directly depend on the 

quality of the input document. The more limited the 

input, the better the performance of the OCR system. But 

when it comes to the completely unrestricted 

handwriting performance of the OCR engine it is 

questionable. Figure 2.3 shows a schematic 

representation of the various character recognition areas 

[2]. 

 

Figure 2 Schematic of Optical Character Recognition 

(OCR.) Area) 

 

c. Template Matching Correlation 

Template matching is a technique in digital image 

processing that has a function to match each part of 

an image with the image that becomes the 

template/reference [4]. This is done by comparing 

the input image with the template image in the 

database, then looking for similarities using a 

certain rule. The image matching process that 

produces a high level of similarity / similarity 

determines that an image is recognized as one of the 

template images. 

 

d. Kiln Manufacture 

Furnace or also often referred to as a combustion 

furnace is a device used for heating. The name 

comes from the Latin Fornax, oven. Sometimes 

people also call it a kiln. A kiln is a tool or 

installation designed as a place of combustion using 

certain fuels that can be used to heat something [1]. 

The furnace is simple, composed of stones arranged 

so that the fuel is protected and heat can be directed. 

In manufacturing companies, the furnace is made in 

such a way that the fire or heat that is formed is not 

too dangerous for the user. 

 

Klin At PT XYZ, the kiln used is a Single Layer 

Tunnel Kiln which consists of 6 combustion zones, 

namely sub dryer, pre heating, firing, rapid cooling 

and cooling, with asbestos insulation and using 

LNG (Liquid Natural Gas) as fuel. The fuel will 

produce heat energy for the ceramic tile burning 

process. The heat that has been used in the firing 

section is not completely removed. Most of it will 

be reused to flow to the dryer and sub dryer. But 

there is also some heat energy that is wasted 

because it contains carbon which can affect the 

results of tile products. 

 

Image 

Acquisition 

(Image Capture) 

Image Quality 

Improvement 

Image 

Representative 

Process 



 

55 | Vol.3 No.2, July 2022 

 

 

 

2.2 Research Method 

This research is applied research to identify 

character in image in kiln machine by using Optical 

Character Recognition (OCR) method. Based on the 

identification of problems obtained in the field 

observation process, literature study and interviews, 

namely during the combustion process in the kiln 

engine, so an application system was created to convert 

image data into alphanumeric data, using the Optical 

Character Recognition (OCR) method based on 

template matching, which then results the conversion 

can be saved into a database. The research method can 

be seen in Figure 3. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3 Research Flowchart Block 

 

2.3 Prototype Architectural Design 
Figure 4 depicts the prototype architecture for 
monitoring the parameters of the ceramic tile 
combustion engine (kiln manufacture) for intensive 
monitoring of the engine. 

 

 
Figure 4 Prototype Architecture 

 

The prototype of this OCR data processing application 

was made by following the steps shown in Figure 5 

below: 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. System Workflow 

 

2.4 Test Design 

The result of the trial on the application is the result 

parameter in the conversion of image data into 

alphanumeric data using OCR. The resulting 

parameters will be used as a basis for predicting the 

value that will come out. In order to know how accurate, 

the prediction will be, before the parameter is formed, 

the parameter is first evaluated and validated by using a 

confusion matrix calculation consisting of Accuracy, 

Precision and Recal [5]. The confusion matrix table can 

be seen in table 1. 

 
Tabel 1 Tabel Confusion Matrix 

 

  True Value 

  TRUE FALSE 

Predicted 

Value 

TRUE 

TP FP 

(True Positive) (False Positive) 

Corect Result Unexpected Result 

FALSE 

FN TN 

(False Negative) (True Negative) 

Missing Result 
Corect Absence 

Of Result 

 

So, the Precision, Recall and Accuracy formulas 
can be seen in formulas 1, 2 and 3. 

Precision = 
𝑇𝑃

𝑇𝑃+𝐹𝑃
  1 

 

Recall = 
𝑇𝑃

𝑇𝑃+𝐹𝑁
  2 

 

Accuracy =  
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
 3 

 
 

 

 

Image 

identification 

Preprocessing 

Result 

Identification 

Developing application 

models 

Prototyping 

Dokumantatio 

Capture 

Image 
Segmentation  

Grayscaling 

and 

binaryization 

Optical Character 

Recognition 

Extracted 

Text 

 



 

56 | Vol.3 No.2, July 2022 

 

 

III. RESULTS AND DISCUSSION 

3.1 Preparation of Training Data and Test Data 

In this study, as many as 100 image data taken from the 
kiln machine in .png format which will later be prepared 
for training data and test data which is the result of web 
capture which will later be converted into alphanumeric 
data. The figure contains parameters that must be 
monitored intensively for 24 hours. These parameters 
contain the temperature, gas pressure and air humidity 
which are read by sensors in certain parts along the 
ceramic tile combustion engine. At this writing, 10 of 
the 39 parameters were taken as samples for testing the 
conversion of images to alphanumeric data using OCR 
based on template matching. In detail can be seen in 
figure 6 table 2 below. 

 

 
 

Figure 6 Capture images of the kiln machine. 

 
Tabel 2 Description Sample parameters for the OCR 

conversion process. 

NO Parameter 
Description 

1 TR1 

Preheating Zone, which is the 

initial zone or area for the product 

to be heated before being burned 

in the next zone. The unit used is 

degrees Celsius (ºC). 

2 TR3 

Is firing zone No.1 compaction 

process (pressure) at high 

temperatures so that changes in 

microstructure occur. In this 

parameter the unit used is degrees 

Celsius (ºC). 

3 TC2 

It is a thermocontroller parameter 

no2 to measure the temperature in 

the combustion process in the 

zone before firing zone no 3. The 

unit used is degrees Celsius (ºC). 

4 TC3 

In this zone, thermocontroller 

parameter no 3 is used to measure 

the temperature in the combustion 

process in the zone before firing 

zone no 4. The unit used is 

degrees Celsius (ºC). 

5 TR14 

Is the zone after passing through 

the cooling zone no. 3 or to lower 

the temperature before entering 

the sub dryer. The unit used is 

degrees Celsius (ºC). 

6 TR17 

It is a drying area (sub dryer) after 

cooling the product. The 

parameter unit used is degrees 

Celsius (ºC). 

7 KCH 

Is a parameter to see the hydraulic 

pressure (hydraulic kiln car) in 

running the conveyor while the 

machine is running. The unit used 

is kg/cm² 

8 TI3 

Is the area to measure the 

temperature of the product no. 3 

(zone temperature) before the 

product comes out of the machine 

after the drying process. The 

parameter unit used is degrees 

Celsius (ºC). 

9 TI7 

Is an area to measure the product 

temperature no. 7 (zone 

temperature) before the product 

comes out of the machine after 

the drying process. The parameter 

unit used is degrees Celsius (ºC). 

10 HI3 

Is a sensor to measure the 

humidity of the air in the kiln 

machine in zone no 3 (Zone 

humidity) 

 
3.2 Modelling 

Making a model or prototype at this writing, the author 

makes a prototype which is divided into 3 parts, the first 

for processing OCR data using thinker board from 

Asus, the second dummy or imitation to display the 

image of the kiln machine using Oracle VM 

VirtualBox, and the third prototype for the application 

server. The OCR uses Oracle VM VirtualBox to 

represent it as a documentation server. 

 

3.3 Character Recognition 

The OCR system was created using the Tesseract OCR 

software which was run on python 3.7 for OCR 

recognition from converted and segmented binary 

images. Tesseract OCR will recognize each character 

from the segmentation results in the image after 

previously training the character template. The process 

of recognizing the character of the kiln machine image 

on the template uses four parameters when initialized, 

namely data path, language, mode, and white list [6] so 

that to obtain accurate detection results, a template is 

created as a path source as shown in Table 4.3. below 

this: 

 



 

57 | Vol.3 No.2, July 2022 

 

Table 3 Implementation of the  

Tesseract OCR Template 

No 
Citra Hasil 

Segmentasi 
Template Karakter 

1   
0 

2   
1 

3   
2 

4   
3 

5   
4 

6   
5 

7   
6 

8   
7 

9   
8 

10   
9 

11   
. 

 

3.4 OCR Application Research Results 

The results of the research on the prototype model of 

data conversion using OCR on the kiln machine PT. 

XYZ can be seen from table 4 for Type A tile products 

and table 5 for MAROON 1000918 products below: 

 
Tabel 4 OCR conversion result for Type A tile products 

 

 

 

 
 

Tabel 6 OCR conversion result for product 

MAROON100918 

 

 
 

3.5 OCR Implementation Test Results 
The test results of the OCR conversion prototype model 
on the kiln machine that have been made using 11 
training data as in table 4.2 and 100 test data containing 
1000 parameters which are divided into two types of 
tasks / products can be seen in table 4.4 as many as 520 
parameters for type A tile products and in table 4.5 there 
are 480 parameters for the maroon 100918 tile product. 



 

58 | Vol.3 No.2, July 2022 

 

The results of testing the application of OCR can be 
seen in table 7 below. 
 

Table 7 Confusion Matrix Test Results 
 

 

Result Class 

current Result 

Class 

current 

current 520 0 

Result 0 480 

 
From table 5 and table 6 above, both the parameters of 
the Type A roof tiles and Maroon100918 products 
obtained accurate conversion results and it is certain 
that there are no inappropriate parameters. From table 
7, the following accuracy is obtained: 

 
Accuracy   = 𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁

 

 
 = 520+480

520+480+0+0

 

 

 = 1000

1000

 

  = 1 x 100% 

                = 100 % 
Based on the results of these calculations, the 

accuracy of 100.0% is obtained. 

 
IV.CONCLUSION 

Based on the discussion of the results of the 

research and testing of the research above, it can be 

concluded that the application model for converting 

images to numerical data using the Optical character 

recognition (OCR) method based on template matching 

obtained an accuracy of 100.00%. 

 

REFERENCES 

[1] Arnofiandi, M. S. (2020) ‘Rancang Bangun Tungku 
Pemanas Dalam Proses Metalurgi Serbuk’, 
Pembelajaran Olah Vokal di Prodi Seni Pertunjukan 
Universitas Tanjungpura Pontianak, 28(2), pp. 

[2]  Chaudhuri, A., Mandaviya, K., Badelia, P., and Ghosh, 
Soumya K. (2017) Optical character recognition 
systems, Studies in Fuzziness and Soft Computing. doi: 
10.1007/978-3-319-50252-6_2 

[3]  Putra, D. (2010) ‘Pengolahan Citra Digital’. Yogyakarta: 

C.V Andi Offset (Penerbit Andi), p. 420. 

[4]  Hossain, M. A. and Afrin, S. (2019) ‘Optical Character 

Recognition based on Template Matching’, Global 

Journal of Computer Science and Technology, 19(2), pp. 

31–35. doi: 10.34257/gjcstcvol19is2pg31 

[5] Andono, P. N., Sutojo, T. and Muljono (2017) 

Pengolahan Citra Digital. Yogyakarta: Penerbit Andi. 

[6] Nunamaker, B. Bukhari, S., Borth, D. And Dengel, A. 

(2016) ‘A Tesseract-Based Ocr Framework For 

Historical Documents Lacking Ground-Truth Text 

German Research Center for Artifical Intelligence 

(DFKI) Kaiserslautern , Germany Univeristy of 

Kaiserslautern , Gemany’, IEEE International 

Conference on Image Processing (ICIP), pp. 3269–3273 

 


