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Identification of Socio Economic Registration Data Using OCR 

Based Tesseract and Google Cloud Vision 
 

Lionardi Ursaputra Pratama 1*, Aviv Yuniar Rahman 2, Rangga Pahlevi Putra 3 
1 2 3 Department of Informatics Engineering, Faculty of Engineering, Universitas Widyagama Malang, Indonesia  

E-mail: ursaputra@cordelia.id*, aviv@widyagama.ac.id, rangga@widyagama.ac.id 

 

 

Abstract:  

The Indonesian government program, called Socio-Economic Registration (Regsosek), aims to measure 

and monitor the socio-economic conditions of low-income people. One of the relevant data used for 

research is Regsosek. This method is used to analyze the influence of economic and social infrastructure 

on economic growth, analyze the socio-economic determinants of ownership of work accident insur-

ance for informal workers, create a women's socio-economic vulnerability index (IKSEP), and study 

intercultural literacy from a social, economic and political perspective. The success of the government's 

Socio-Economic Registration program depends on the role of data collection officers or surveyors, who 

directly interact with the community to obtain information about Socio-Economic Registration (Reg-

sosek) data collection. This method also has other obstacles that significantly affect the overall results 

of the survey, where the survey results must be entered manually by the surveyor from a form with 

handwritten data, after which it is entered into the website. This method is vulnerable to human error, 

where the handwriting is difficult to read, and mistakes are made during the data input. The technology 

that can be used to handle this problem is implementing the OCR method, where writing that was ini-

tially handwritten manually can be identified and converted into digital text that can be edited (editable 

text) and processed automatically. This research shows that the proposed method has good accuracy, 

with an Accuracy of 96.45%, CER 0.3%, and WER 4.30%. 

 

Keywords: Hand Writing, Optical Character Recognition, Socioeconomic infrastructure, Surveyor of-

ficer. 

 

I. Introduction 

The Indonesian government program, called Socio-Economic Registration (Regsosek), aims to 

measure and monitor the socio-economic conditions of low-income people. According to this program, 

social protection is the government's way of dealing with poverty in Indonesia [14]. One of the relevant 

data used for research is Regsosek. This method is used to analyze the influence of economic and social 

infrastructure on economic growth, analyze the socio-economic determinants of ownership of work 

accident insurance for informal workers, create a women's socio-economic vulnerability index (IKSEP), 

and study intercultural literacy from a social, economic and political perspective [11]. 

The success of the government's Socio-Economic Registration program depends on the role of data 

collection officers or surveyors, who directly interact with the community to obtain information about 

Socio-Economic Registration (Regsosek) data collection. So that the public as respondents can provide 

answers that are appropriate to the conditions, this must be explained well in the field. However, there 

are obstacles to getting transparent information from respondents. This method also has other obstacles 

that significantly affect the overall results of the survey, where the survey results must be entered man-

ually by the surveyor from a form with handwritten data, after which it is entered into the website. This 

method is vulnerable to human error, where the handwriting is difficult to read, and mistakes are made 

during the data input [12]. 

The technology that can be used to handle this problem is implementing the OCR method, where 

writing that was initially handwritten manually can be identified and converted into digital text that can 

be edited and processed automatically (Memon et al., 2020). Before the system can recognize human 

P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.5 No.2 June 2024 
Buana Information Technology and Computer Sciences (BIT and CS) 
 



Vol. 1, No.2 June 2024 | 65  

 

handwritten image patterns, information representing the image must be retrieved, known as input data. 

A scanning process is carried out on the resulting image to obtain digital data, and then a preprocessing 

process is carried out [7]. This process can complete the creation of an intelligent computer system that 

can recognize handwriting. Often, these methods are combined with other algorithms to create new 

applications that can solve more complex problems [5]. 

Currently, OCR is applied in various fields, including data entry. Previous research utilized OCR 

as automation for logistics warehouse data input management [2]. The results of the introduction of 

OCR are used as input for the logistics warehouse management system, which will then be processed 

by the management system and entered into the database. In detecting handwriting, this research 

produces an accuracy of 46.9%. It shows that Tesseract can be implemented to convert images into text. 

However, the recognition results depend on the quality of the image and the variety of text used as test 

data and taking images requires sufficient light. Other research uses Tesseract OCR for text recognition 

in social survey data. This research produced a CER of 2.60% and a WER of 25.20% using the Tesseract 

method [1].  

The difference in accuracy and error rate is due to the library's dependence on the test document's 

quality, so the OCR output is often inappropriate. Both studies show that the quality of the image or 

document used as a source scanned by an OCR application is the main problem in using OCR on 

handwritten text. 

Tesseract is an open-source OCR application created by HP from 1984 to 1994. Tesseract was first 

created as a graduate project and released by Hewlett Packard and the University of Nevada, Las Vegas, 

in 2005. Now, Google partially funds its development, and version 2.0, Tesseract 4. x, was released in 

December 2019. Tesseract has four main functional modules: static character classifier, word 

recognition, paragraph and sentence search, linguistic analysis, and adaptive classifier. However, if the 

image is preprocessed, the results from Tesseract will be much better. 

To overcome this problem, research was carried out to automate data input in handwriting from 

survey forms using the OCR method based on Google Vision and Tesseract. This research aims to 

create a system that can be used to recognize handwriting so that it can be easily recognized as text 

using computer vision methods. 

II. Methods 
This research is divided into several general stages. This stage includes literature study, planning, 

data collection, implementation, testing, and report preparation. These stages will be carried out 

sequentially to produce an optimal report. The general stages of research can be seen in Figure 1. The 

literature study phase involves a comprehensive review of existing research, theories, and relevant 

publications to establish a solid theoretical framework for the study. 

 
Fig1. Study Method 

A.    Literature Study 

In this research, the references used come from books, journals, ebooks, previous research and 

other reliable sources. The literature study used Google Scholar as the primary search tool for references 

related to text detection, Tesseract and Google Vision. Literature research was carried out to obtain 

information about several main topics of this research, namely: 



Vol. 5, No.2 June 2024 | 66  

 

a) OCR 

b) Google Cloud Vision 

c) Tesseract 

d) Merged Method 

B.   Data Gathering 

The required data collection stage is the most important in this research. The data collected is used 

for system testing, whether the method used is optimal for the test data. The survey form image data 

was collected by scanning 100 data. 

C. OCR Implementation 

Based on Figure 2, this research began by entering the Survey Data image. After that, the image is 

processed for thresholding and greyscale. This pre-processing is done so OCR can more easily 

differentiate between objects and backgrounds. Next is detecting text; Tesseract and Google Vision are 

used to extract images into ASCI characters. After the ASCI characters are obtained, compare the results 

of the two methods, and which is the best with Ground Truth? Then, the best results will be normalized 

and entered into the ignore list process to determine which words will be used and which will be ignored. 

The following process is testing, which will use the CER, WER and Accuracy parameters. These 

metrics will assess the performance of the OCR methods and provide valuable insights into their 

effectiveness in accurately converting the images into editable text, thereby ensuring the reliability of 

the data obtained from the survey images. Calculations of accuracy, Word Error Rate (WER), and Co-

efficient of Error Rate (CER) can be made using formulas 1, 2, and 3, respectively. 

 
CER = (Insertion + Removal + Substitution)/TotalnumberofcharactersinGroundTruth (1) 

𝑊𝐸𝑅 = (𝐼𝑛𝑠𝑒𝑟𝑡𝑖𝑜𝑛 + 𝐷𝑒𝑙𝑒𝑡𝑖𝑜𝑛 + 𝑆𝑢𝑏𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛)/𝑇𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑤𝑜𝑟𝑑𝑠𝑖𝑛𝐺𝑟𝑜𝑢𝑛𝑑𝑇𝑟𝑢𝑡ℎ (2) 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (1– 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝐶𝐸𝑅) ∗ 100 (3) 

 

 
Fig 1. Optical Character Recognition Process 

These assessments were fundamental in ensuring the fidelity and precision of the data derived 

from the survey images, thereby bolstering the overall quality and trustworthiness of the research out-

comes. 

 

III. Results  
A.    Literature Study 

a. Optical Character Recognition (OCR) 

Optical Character Recognition (OCR) is an application that can recognise ASCII characters in 

digital photographs and turn them into editable text data [6].  

As seen in Figure 3, the OCR procedure often comprises multiple steps. The three main processes 

in this procedure are preprocessing, feature extraction, and recognition. Preprocessing entails improving 



Vol. 1, No.2 June 2024 | 67  

 

the image and reducing noise, feature extraction finds essential elements, and recognition converts these 

elements into text [9]. 

 
Fig 2. OCR Process Flowchart 

b. Tesseract 

Tesseract is a widely used optical character recognition (OCR) library for processing scanned im-

ages for text recognition. This engine uses neural networks to analyze image patterns and recognize 

characters with high accuracy. This library can recognize characters in various languages, such as Ara-

bic, Bulgarian, Catalan, Chinese, Czech, Danish, Dutch, English, French, German, Greek, Hindi, Indo-

nesian, and Italian. Some studies have suggested using convolution-based preprocessing with specific 

kernels to improve the accuracy of the Tesseract OCR engine [3]. 

 

 
Fig 3. Architecture of Tesseract Library 

First, the text area in the image is identified through page layout analysis. Next, the found text 

areas are divided into several "blobs". Blobs are classifiable units consisting of multiple characters or 

parts of multiple characters [10]. The third step is to determine the lines of text and combine the blobs 

into a series of words that fill the space. The first step is to prepare the words for recognition. The next 

step is to recognize each word in two routes. Tesseract breaks down and merges the single word in each 

path into blobs, forming a series of identifiable character outlines. Tesseract recognizes the character 



Vol. 5, No.2 June 2024 | 68  

 

outline on the first computer with a static classifier based on the feature library. The architecture of 

Tesseract is depicted in Figure 4. 

 

c. Google Vision 

 
Fig 4. Flowchart of Google Vision 

Google Vision is a computer vision technology developed by Google that uses machine learning 

to analyze and understand visual information in images and videos. This technology offers many 

features, such as image processing, face processing, object detection, and labelling. The Google Cloud 

Vision API allows developers to integrate this service into their applications [4]. The utilization of 

Google Vision can be implemented, as shown in Figure 5. 

B.   Implementing Method 

The end product of this research is an application that can be used to extract text from handwritten 

text photographs. Python is the programming language used to create the program, and it runs on the 

Google Colab platform. Google Colab produces an Excel file with the file name and detection results 

as part of its output. And the outcomes of detection. The Excel file's detection findings are anticipated 

to make it easier for surveyors to interpret handwritten responses on survey sheets. The user will be 

prompted to input the image directory and the output file name in the main program, which is an Excel 

file. Figure 6 displays the program display. 

 

 
Fig 5. Input and Output Program 

 

Setting up the Google Cloud Vision API service credentials using the GOOGLE_APPLICA-

TION_CREDENTIALS environment variable completes the procedure. Next, the input directory's 

image file list (img_files) is initialised. 

One method of obtaining truth data is using an Excel file, previously mentioned in 

ground_truth_file. The file's text data is loaded into a Pandas data frame, and the 'text' column is ad-

justed to change the text to lowercase (lowercase). Then, in identifying text in photos, a list of words 

(ignore_list) that will be disregarded includes pointless or useless terms. Every picture file in the 

img_files directory undergoes one iteration. Tesseract OCR with Google Cloud Vision API's OCR (Op-

tical Character Recognition) technique extracts text from each read image. 

Additionally, word similarity between the resultant text and the truth text (ground_truth_text) was 

determined using the Natural Language Toolkit (NLTK) library's CER (Character Error Rate) and WER 

(Word Error Rate) metrics. The text detection findings are displayed as graphics with distinct colour 

markers for each found word. 

For every word in ground_truth_df, an iteration loop completes the process. The average is then 

determined by adding up the findings of the CER and WER measurements. The application then uses a 

Pandas DataFrame-which will be added or built if it does not exist-to store the detection results in an 

Excel file. 



Vol. 1, No.2 June 2024 | 69  

 

The print programme averages the CER and WER from the Tesseract technique, Google Vision, 

and a mixture of both after all photos have been analysed. The accuracy of each approach is then printed 

by the programme as well. This program records and saves the detection findings into an Excel analysis 

file for additional study. It also gives a comprehensive report on the accuracy of the text detection results 

from the two OCR algorithms utilised. Figure 7 displays certain outcomes from the programme output. 

 

 
Fig 6. Output results and pre-processing 

The ignore list condition is used in the subsequent findings, as shown in Table 1. During the pre-

processing step, the Otsu approach gives the image better contrast, eliminates noise, and facilitates pro-

gram processing. All the text in the image is readable correctly when viewed from the output the pro-

gramme generated using the suggested way. 

 

Table 1. Text Extraction Result 

Ground Truth Ignore List Result 
- 101. Provinsi - 

- 102. Kabupaten /Kota *) - 

- 103. Kecamatan - 

- 104. Desa/Kelurahan*) - 

- 105. Kode SLS/Non SLS - 

- 106. Nama SLS/Non SLS - 

- 107. Alamat (Jalan/Gang, 

Nomor Rumah) 

- 

JAWA TIMUR - JAWA TIMUR 

35 - 35 

MALANG - MALANG 

73 - 73 

BLIMBING - BLIMBING 

040 - 040 

PURWODADI - PURWODADI 

008 - 008 

0057 - 0057 

Kode Kode - 

Sub SLS Sub SLS - 

00 - 00 

RT. 007 - RT.007 

RW. 008 - RW.008. 

JL Plaosan Barat 83 - JI Plaosan Barat 83 

 

In comparing image processing methods as shown in Table 2, Data 1 uses a more straightforward 

image processing technique: grayscale on the tested images. Meanwhile, in Data 2, the method applied 



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is more sophisticated, applying the Otsu Thresholding method. Otsu Thresholding is a technique used 

to segment images by determining thresholds automatically based on image histogram analysis. Using 

grayscale in Data 1 means only using the brightness channel of the image, ignoring colour information. 

Although simple, grayscale can be helpful in some cases, especially when colour accuracy is not the 

central aspect required in text or object recognition. 

 

Table 2. Otsu Tresholding Implemented and Grayscale Only 

File Name CER Difference WER Difference 
Accuracy 

Difference 
data-1.jpg 0.23% (Increase) 1.05% (Increase) -0.23% (Decrease) 

data-2.jpg -0.27% (Decrease) -0.42% (Decrease) 0.27% (Increase) 

data-3.jpg 0.18% (Increase) 0.21% (Increase) -0.18% (Decrease) 

data-4.jpg -0.08% (Decrease) 0.20% (Increase) 0.08% (Increase) 

data-5.jpg 0.13% (Increase) -0.42% (Decrease) -0.13% (Decrease) 

data-6.jpg -0.35% (Decrease) -0.85% (Decrease) 0.35% (Increase) 

data-7.jpg 0% 0% 0% 

data-8.jpg 0% 0.42% (Increase) 0% 

data-9.jpg 0.23% (Increase) -0.19% (Decrease) -0.23% (Decrease) 

data-10.jpg 0.29% (Increase) 0.42% (Increase) -0.29% (Decrease) 

 

On the other hand, applying Otsu Thresholding to Data 2 can provide advantages because this 

method can automatically determine the optimal threshold for separating objects from the background. 

The table shows the results of a comparative analysis of the performance of Tesseract and Google Vi-

sion in terms of Character Error Rate (CER), Word Error Rate (WER), and Accuracy differences be-

tween ten different data pictures for Optical Character Recognition (OCR). The two approaches in each 

photograph show apparent differences in performance metrics. For example, Tesseract and Google Vi-

sion both show higher CER and WER in data-1.jpg, which is a factor in Tesseract's decreasing accuracy. 

On the other hand, Google Vision keeps its CER constant while somewhat increasing its WER. The 

combined data show a modest loss in overall accuracy along with a minor rise in CER and WER. 

Both approaches show a drop in CER and WER in data-2.jpg, with Tesseract showing a more 

significant improvement over Google Vision. As a result, Tesseract's accuracy increases noticeably, 

while Google Vision's accuracy slightly increases. The combined results show a drop in WER and CER; 

however, accuracy has somewhat decreased. From data-3.jpg to data-10.jpg, the remaining photos show 

consistent patterns of volatility. Specifically, differences in CER, WER, and accuracy point to distinct 

advantages and disadvantages between Tesseract and Google Vision on various datasets. Using this 

technique, images may undergo better segmentation, improving the system's ability to better recognize 

text or objects in the image. 

 

Otsu Method 

The Otsu method uses discriminant analysis to find variables that distinguish between two or more 

naturally occurring groups [15]. Discriminant analysis maximizes these variables to divide objects into 

foreground and background. The discriminant analysis produces a threshold value that divides a 

grayscale image into two groups of black and white [13]. 

C.   Analysis 

The combination approach proved to be more effective than either one alone. While Tesseract and 

Google Vision have somewhat higher average CERs-roughly 1.8% to 1.9% for Tesseract and 1.0% to 

1.7% for Google Vision-the combined method's average CER is 1.8% to 1.9%. 

For the same reason, the combined approach performs better when calculating the Word Error Rate 

(WER). The combined method's average WER ranges from 1.9% to 2.0%, Tesseract's from 2.0% to 

2.5%, and Google Vision's from 1.7% to 2.2%. The error rate in text detection in photos is successfully 

decreased by combining the output of two OCR algorithms. This demonstrates that using Google Vision 

in conjunction with Tesseract OCR can yield better accurate results regarding text recognition on photos 

than either technique alone. 



Vol. 1, No.2 June 2024 | 71  

 

As a result, the program's output results demonstrate that the combined approach can identify text 

on images with a greater accuracy rate, making it a better option for text recognition applications 

requiring a low error rate. 

Discussion 

Reference Novelty Results of Previous 

Research 

Results of the Pro-

posed Method 

(Arianto et al., 2023) Difference: 

Previous research pro-

duced a CER of 2.60% 

and a WER of 25.20% in 

detecting text and did not 

include the accuracy ob-

tained in the test. We in-

vestigated how to reduce 

the CER and WER values 

and obtain high accuracy 

in detecting written text. 

hand. 

Novelty: 

This research shows that 

the combination of OCR 

using Google Vision and 

Tesseract is a method 

with a lower WER level, 

and there is a decrease in 

CER and WER compared 

to before. 

CER: 2.60% 

WER: 25.20% 

Accuracy: - 

CER: 0.3% 

WER: 4.30% 

Accuracy: 96.45% 

(Berg, So and Seo, 

2019b) 

 

Difference: 

This research only pro-

duces an accuracy of 

46.9% in detecting hand-

writing, we analyze how 

accuracy in detecting 

handwriting can be im-

proved. 

Novelty: 

This study shows that the 

most accurate OCR ap-

proach and also shows 

improvements compared 

to previous methods is the 

combination of Tesseract 

and Google Vision. 

CER: - 

WER: - 

Accuracy: 46.9% 

(Thammarak et al., 

2022) 

Difference: 

This research only pro-

duces accuracy on Tes-

seract of 47.02% and 

produces an accuracy of 

84.03% in experiments 

using Google Vision in 

detecting writing. We an-

alyze how accuracy in 

detecting handwriting 

Tesseract 

CER: - 

WER: - 

Accuracy: 47.02% 

 

Google Vision 

CER: - 

WER: - 

Accuracy: 84.03% 



Vol. 5, No.2 June 2024 | 72  

 

can be increased by com-

bining the two methods 

based on evaluations 

from previous research. 

Novelty: 

This study shows that the 

most accurate OCR ap-

proach and also shows 

improvements compared 

to previous methods is the 

combination of Tesseract 

and Google Vision. 

 

Table 4.4 illustrates a comparison of the results of this study with previous research. Previous 

research, without considering accuracy, recorded CER and WER for text detection of 2.60% and 

25.20%. In an effort to achieve high levels of accuracy, as well as lower CER and WER, this study 

shows that utilizing Tesseract and Google Vision together for OCR can reduce WER values and increase 

accuracy up to 96.45%. On the other hand, previous research only achieved an accuracy of 46.9% in 

identifying handwriting. However, the results of this research show that combining Tesseract with 

Google Vision is the most accurate OCR solution and provides significant progress compared to previ-

ous methods. 

It is important to note that the collaboration of the two OCR methods not only results in a higher 

level of accuracy, but also provides significant progress compared to previous studies. These final re-

sults confirm that the combined approach between Tesseract and Google Vision is not only effective in 

reducing WER values, but also creates a superior OCR solution in recognizing handwriting, overcoming 

the obstacles faced by previous methods. 

 

IV. Conclusion 
The program uses Tesseract OCR, Google Vision API, and combination approaches to recognise 

text on images. The study of the program's output results concludes that the combined method better 

recognises text on photos. While both Google Vision API and Tesseract OCR have reasonable accuracy 

rates, combining the two results in a far lower mistake rate regarding text recognition. 

Compared to individual approaches, the combined method successfully demonstrated lower 

average CER (Character Error Rate) and WER (Word Error Rate). This demonstrates how text 

identification accuracy in photos can be increased using a method that combines the outcomes of 

Tesseract OCR with Google Vision. Therefore, using integrated approaches can be a more efficient and 

best option for applications that need text recognition with a low mistake rate. This conclusion 

demonstrates that combining different techniques can yield more dependable and accurate results when 

it comes to text recognition in photos. 

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