Changing Data Image Into Numeric Data on Kiln Manufacture Machinery Use Optical Character Recognition (OCR)

— 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,


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.
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.

. 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.

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].

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.

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 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:

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

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).

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).

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).

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).

TR17
It is a drying area (sub dryer) after cooling the product. The parameter unit used is degrees Celsius (ºC).

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).

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).

HI3
Is a sensor to measure the humidity of the air in the kiln machine in zone no 3 (Zone humidity)

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.

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:

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

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  = 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%.