Utilization of Optical Character Recognition (OCR) in the

Indian Journal of Science and Technology, Vol 12(16), DOI: 10.17485/ijst/2019/v12i16/137794, April 2019

ISSN (Print) : 0974-6846

ISSN (Online) : 0974-5645

Utilization of Optical Character Recognition (OCR)

in the Development of a Number System Converter

Application

Niel Francis B. Casillano

Department of Information Technology, Eastern Samar State University Philippines; nf[email protected]

Keywords: Android, Mobile Technology, Number System, ISO, OCR

Abstract

Objectives: This study focused on integrating Optical Character Recognition (OCR) in the development of a Number System

Converter Android application. The primary goal of the study was to read the written or printed text either in decimal,

binary, hexadecimal and octal and convert it to the desired number system. Methods: The Rapid Application Development

methodology was employed in the development of the system. Findings: The expert testing resulted in a grand mean of

4.77, interpreted as strongly acceptable. This means that the expert evaluators were able to use the system with ease and

was easily able to convert text to the desired number system successfully. Applications/Improvements: The successful

development of the application will immensely help students to easily convert a value from one number system to another.

1. Introduction

Information technology has revolutionized and trans-

formed how people deal with the economy, business,

politics, education, religion, and many other elds. Every

day more and more innovations are developed with the

primary aim to provide convenience with the way people

learn and communicate

. One of the major breakthroughs

that made a huge impact in many elds is the develop-

ment of mobile and wireless technology. One of the elds

that have been greatly impacted by mobile technology is

the academe. is started the shi of many educational

institutions from a traditional setting to a mobile learning

setting.

One of the emerging technologies that are being

utilized by many mobile application developers is the

OCR. OCR is the mechanical or electronic conversion

of images of typed, handwritten or printed text into

machine-encoded text, whether from a scanned

document, a photo of a document, a scene-photo or from

subtitle text superimposed on an image

. It is widely used

as a form of information entry from printed paper data

records, whether passport documents, invoices, bank

statements, computerized receipts, business cards, mail,

printouts of static-data or any suitable documentation.

Meanwhile, one of the most cognitively challenging

topics in computer science is the topic on number system

conversion. Number system conversion involves convert-

ing a value from one base to another other (e.g. decimal

to octal, decimal to binaryf5 and vice versa). All num-

ber systems are interconvertable, each having a dierent

method to be converted

. e long conversion methods

become very tedious for beginners making it hard for

them to understand and surpass the number system con-

version topic

e mobile technology revolution and the OCR

technology have prompted the researcher to develop a

mobile application that utilized the capabilities of Optical

Character Recognition. e application served as an

automated number converter but instead of typing the

data the application was integrated with a camera that

reads a handwritten text and automatically converts it to

Indian Journal of Science and TechnologyVol 12 (16) | April 2019 | www.indjst.org

Utilization of Optical Character Recognition (OCR) in the Development of a Number System Converter Application

the desired number system (either decimal, binary, octal

or hexadecimal).

2. Objectives of the Study

is study primarily aimed to integrate OCR in the devel-

opment of an Android-based Number System Converter

Application. Specically, this study aimed to:

1. Develop and integrate the following features:

a. A module for choosing the base number system to the

desired number system.

b. A component for capturing written text.

c. A module for showing the converted value.

2. Conduct an Expert Testing using a modied question-

naire based on ISO 9126 Soware Quality Metrics

3. Software Development

Methodology

e development of the system adapted the Rapid

Application Development (RAD) model. Rapid appli-

cation development is a soware methodology which

provides a faster development and does not compromise

the quality of the soware

.As shown in Figure 1, RAD

is divided into three major stages: (1) requirements plan-

ning; (2) system design; and (3) system implementation

7,8

Figure 1. Rapid application design framework

3.1 Requirement Planning Phase

e developers conducted on-site observations and inter-

views to be familiar with the transactions of the manual

system and assess the needs of the users. System require-

ments were then identied to t the needs of the users.

3.2 RAD Design Workshop Phase

e developers developed module designs and integrate

them into a functional system. e module designs and

functions were updated depending on the user responses.

e process was done over and over until a rened ver-

sion of the system was developed.

3.3 Implementation Phase

e rened versions were then tested by the end-user/

clients for nal quality check and nal implementation

is carried out once the system adheres to the end-users’

standards.

Figure 2 shows the dierent capabilities of the applica-

tion. As shown in the use case diagram, the user can open

the application, choose the desired number system and

ultimately capture the written value; the application will

then automatically convert the captured value and display

the nal output.

Figure 2. Use case diagram for the number system

application.

3.4 Design Requirements

As shown in Figure 3, the dierent steps in optical charac-

ter recognition are as follows:

1. First step in OCR is the Scanning of images and con-

verts it to its corresponding digital value. is process

is called text digitization

2. Pre-processing is the application of dierent algo-

rithms and techniques to make raw data more usable.

3. Noise Detection and Correction is the removal of

unwanted data from the converted images and text to

ensure that a more usable digital data is captured.

4. Sometimes during the image conversion, digitized

data is skewed; this is why skew detection and correc-

tion is employed to x image angles.

5. e primary aim of layout analysis is to divide the raw

image into non-text areas and “text lines”–sub-images

Indian Journal of Science and Technology

Vol 12 (16) | April 2019 | www.indjst.org

Niel Francis B. Casillano

of the original image that each contains a linear

arrangement of symbols in the target language.

6. Characters are then divided into arcs, circles, and other

geometric gures and are compared to a known char-

acter.

7. Finally, neural networks, decision trees, and other algo-

rithms are employed to produce the actual value of the

character.

Figure 3. Steps of OCR

4. Software Evaluation

e questionnaire that was used to evaluate the system

is based on ISO 9126 standards. ISO 9126 serves as a

framework or model for providing worldwide acceptable

soware qualities required for soware evaluation. Under

this standard, soware must possess six main quali-

ties namely: Functionality, Maintainability, Eciency,

Reliability, Portability and Usability. (ISO, 1991). e

experts were chosen because of their strong prociency

on the eld of mobile computing technology. e experts

were faculty members who handled subjects related to

android technology, java application development and

web applet development.

5. Results and Discussion

Aer undergoing the dierent phases of development and

soware evaluation under the RAD model, the following

results were obtained:

5.1 Interface Design

Figure 4 shows the homo form for the application. e

user can choose whether to convert a value from decimal

(base 10) to binary (base 2), octal (base 8), and hexadeci-

mal (base 16).

Figure 4. Number system converter home screen

Figure 5 shows the interface for converting binary

(base 2) to decimal (base 10) and vice versa. e interface

contains a camera module where the user will point the

mobile phone’s camera to the written decimal or binary

value and convert it to the chosen number system.

Indian Journal of Science and TechnologyVol 12 (16) | April 2019 | www.indjst.org

Utilization of Optical Character Recognition (OCR) in the Development of a Number System Converter Application

Figure 5. Binary to decimal and decimal to binary conversion

screen.

5.2 Expert Testing

Table 1 shows that all experts answered yes (n=15, 100%)

to all items under the parameter functionality. is means

that all components needed by the end-user/client are

present in the mobile application. is also means that

the system has fully adhered to the standards of ISO in

terms of functionality.

Table 2 shows a grand mean of 4.91 interpreted as

strongly acceptable. All ve subparameters were rated

as strongly acceptable (understand ability; learn ability,

operability, attractiveness, usability compliance). is

result implies that the mobile system is indeed usable.

e mobile system can easily be manoeuvred and all its

components and operations. Its basic design also made

the system learnable and user-friendly. e same data also

entails that the mobile system adhered to the standards of

ISO in terms of Usability.

Table 2. Mean and interpretation for parameter

usability

Usability

Sub parameter Mean Interpretation

Understand ability 5.00 Strongly Acceptable

Learn ability 5.00 Strongly Acceptable

Operability 4.92 Strongly Acceptable

Attractiveness 4.92 Strongly Acceptable

Usability compliance 4.69 Strongly Acceptable

Grand Mean 4.91 Strongly Acceptable

Table 3 shows a grand mean of 4.63 interpreted

as strongly acceptable. All three subparameters were

rated as strongly acceptable (Time behavior, Resource

Utilization, and Eciency Compliance). is result

implies that the mobile system is ecient in terms of

delivering results and handling data. is also means that

the mobile system fully adhered to the ISO standards in

term of Eciency.

Table 3. Mean and interpretation for parameter

eciency

ISO 9126 Major

Parameters

Weighed

Mean

Interpretation Rank

Functionality -

Eciency 4.63 Strongly

Acceptable

Usability 4.91 Strongly

Acceptable

Grand Mean 4.77 Strongly

Acceptable

Table 4 shows us an overall mean of 4.77 interpreted

as strongly acceptable. e parameter Functionality, as

shown in Table 1, shows that all the needed requirements

Table 1. Mean and interpretation for parameter functionality

Functionality

Questions Frequency

(Yes)

Percentage

(Yes)

Frequency

(No)

Percentage

(No)

A module for choosing the base number

system to the desired number system.

15 100% 0 -

A component for capturing written text. 15 100% 0 -

A module for showing the converted

value.

15 100% 0 -

Indian Journal of Science and Technology

Vol 12 (16) | April 2019 | www.indjst.org

Niel Francis B. Casillano

based on the requirements analysis were all met; this is

evident with the full agreement of all expert testers. e

major parameters (Eciency and Usability) were rated

by the pool of experts as Strongly Acceptable. e dif-

ferent parameters were ranked based on the weighted

mean to determine the best parameter the mobile system

has and based on table 4 that best feature the system is

its Usability. Although the mobile system was deemed

strongly acceptable improvements can be made so that all

parameters will have a perfect rating and for the mobile

system to be more resistant to errors and bugs. Overall,

the system has adhered to the quality standards of ISO

9126.

Table 4. Grand mean for all parameters

Eciency

Sub parameter Mean Interpretation

Time behaviour

4.77 Strongly Acceptable

Resource Utilization

4.56 Strongly Acceptable

Eciency compliance

4.55 Strongly Acceptable

Grand Mean 4.63 Strongly Acceptable

6. Conclusions

Based on the System Development and Expert Testing,

the following conclusions were drawn:

1. e following features were successfully developed and

integrated into the system, as per results reected on

table 1:

a. A module for choosing the base number system to

the desired number system.

b. A component for capturing written text.

c. A module for showing the converted value.

2. e Expert Testing resulted in a grand mean of 4.77,

interpreted as strongly acceptable. is means that the

system adhered to all sub-parameters under the major

parameters Eciency and Usability. Furthermore,

improvements in the overall design and fault tolerance

are recommended to improve the results of the expert

testing.

7. References

1. Dotong C, De Castro E, Dolot J, Prenda M. Barriers for

educational technology integration in contemporary class-

room environment. Asia Pacic Journal of Education, Arts

and Sciences. 2016; 3(2):13–20.

2. Gosavi M, Pund I, Jadhav H, Gedam S. Mobile application

with optical character recognition using neural network.

Journal of Computer Science and Information Technology.

2015; 4(1):483–9.

3. Latif S, Ullah R, Jan H. A step towards an easy inter

conversion of various number systems. arXiv preprint

arXiv:1107.1663. 2011.

4. Latif S, Qayyum J, Lal M, Khan F. Novel approach to

the learning of various number systems. International

Journal of Computer Applications. 2011; 7(4):18–28.

https://doi.org/10.5120/3116-4283

5. ISO. ISO/IEC FDIS 9126-1: Soware Engineering - Product

Quality -Part 1: Quality Model. Geneva, Switzerland:

International Standardization Organization; 2001.

6. Naz R, Khan M. Rapid applications development tech-

niques: A critical review. International Journal of Soware

Engineering and its Applications; 2015. p. 163–76.

https://doi.org/10.14257/ijseia.2015.9.11.15

7. Kendall KE, Kendall JE. Systems analysis and design. 8th

Edition. New Jersey: Pearson Education, Inc.; 2011. p.

1–601.

8. Sharma A, Jha SK. Identication of alphanumeric pattern

using android. International Journal on Recent and Innovation

Trends in Computing and Communication. 2015; 3(4):1–5.

https://doi.org/10.17762/ijritcc2321-8169.1504149