ANALYSIS OF BAR CODES WITH

DIFFERENT SCANNING DEVICES

Ivan Fung, Nick Formoso, Erika Jinbo, David McNutt, Kevin O’Connor, Stephen Elliott

The purpose of this project was to do a comparative analysis of various bar codes on soil bag samples using different scanning methods (more specifically iPhone 5 vs.

Android smartphones). This project is also referencing a previous analysis/assessment of the Sure-Tech Laboratories’ soil bags using a regular bar code scanner. Using

the samples from the Sure-Tech Laboratories Company, we compared the effectiveness of readability of the bar codes and the data from the regular bar code scanner vs.

smartphone technologies.

SAMPLE DESCRIPTION

OVERVIEW

METHODOLOGY

RESULTS AND DATA ANALYSIS EXAMPLES OF FAILED SAMPLES

Factors Levels Remarks

Symbology

QR Code

PDF417

Data Matrix

X-Dimension

14.2 mil (QR Code & Data

Matrix)

7.5 mil (PDF417)

Error

Correction

Level

Low

Medium

High

QR Code (L, M, H)

PDF417 (4,5,6)

Data Matrix (N/A)

Label

Location

Front

Side

Label Number Symbology Error Correction Location

1 QR Code Low Front

2 QR Code Low Side

3 QR Code Medium Front

4 QR Code Medium Side

5 QR Code High Front

6 QR Code High Side

7 PDF417 4 Front

8 PDF417 4 Side

9 PDF417 5 Front

10 PDF417 5 Side

11 PDF417 6 Front

12 PDF417 6 Side

13 Data Matrix N/A Front

14 Data Matrix N/A Side

The samples used for this

analysis are bar codes

printed on soil sample bags

from Sure-Tech Labs. They

were all acquired from a

previous study conducted.

These samples are varied in

three factors: bar code

symbology, error correction

level, and label location.

Table 1 shows an

explanation of the levels.

The samples are then

further categorized into 14

groups. Each label has a

distinct combination of

symbology, error correction

level, and label location.

Table 2 shows the

characteristics of each label

number.

Table 1: Level of details for bar code

symbology, error correction, and label

location

Table 2: Characteristics of each label

number

Figure 4: Data collection process

The phones are clamped into a rig that

keeps the phone exactly 10 cm from the

scan subject. If the phones do not scan the

bar code within 10 seconds, we consider it

a failed attempt.

Both phones used the application called

“Barcode Scanners” by Manatee Works.

Figure 4 shows a flow chart that shows

how we went through and scanned each

bag.

Table 3: Summary of data entry results Table 3 shows the data we

collected in this research.

Every group of soil bags has

28–30 samples and each

one of them was scanned

five times. We then totaled

the number of successful

scans for every bag within

each group.

Compared to the scanner,

the iPhone and Android had

more 4–5 successful scans

among all bags. But iPhone

and Android also had more

0 successful scans

compared to the scanner.

PDF417 bar codes printed on the front with high error correction had a 100% of

scan pass percentage for all three devices.

iPhone Android Scanner

Label # 0 1-3 4-5 0 1-3 4-5 0 1-3 4-5

1 1 2 26 2 3 24 1 1 27

2 1 0 28 2 1 26 4 9 16

3 1 2 27 1 2 28 0 1 29

4 1 1 28 2 2 26 1 3 26

5 1 7 20 0 0 28 0 11 17

6 2 0 28 2 0 28 1 8 21

7 1 0 29 0 0 30 0 0 30

8 1 1 27 0 1 28 0 3 26

9 0 0 29 3 0 26 0 0 29

10 3 0 27 0 3 27 1 1 28

11 0 0 28 0 0 28 0 0 28

12 7 1 21 9 1 19 1 3 25

13 1 0 28 0 1 28 1 2 26

14 2 1 25 4 1 23 2 0 26

Figure 5: With this QR Code, the scanner, iPhone, and Android

Phone all failed to scan this for the five trials that it was tested.

Figure 5: Sample 1

Figure 6: Sample 2

Figure 6: For this Data Matrix Code, the regular scanner succeeded in

scanning it, but both the iPhone and Android Phone failed to scan it.

Figure 7: Sample 3

Figure 7: For this QR Code, the regular scanner failed to scan it, but

both the iPhone and Android Phone succeeded in scanning it.

Specifications Samsung Galaxy SII

Operating

System

Android 2.3.3 (Gingerbread)

System on

Chip

Samsung Exynos 4 Dual 45 nm (GT-I9100, SHW-M250S/K/L)

CPU 1.2 GHz dual-core ARM Cortex-A9

GPU ARM Mali-400 MP4

Memory 1 GB RAM

Storage 16 GB flash memory

Data Inputs Multi-touch touch screen, headset controls, proximity sensor,

ambient light sensor

Display 4.3 in (110 mm) AMOLED with 480×800 pixels (218 ppi) and RGB-

Matrix

Rear Camera 8 Mpx Back-illuminated sensor with auto focus, 1080p 30 fps full

HD video recording. Single LED flash.

SCANNER DESCRIPTION

Specifications Apple iPhone 5

Operating

System

iOS 6.1

System on

Chip

Apple A6

CPU 1.3 GHz dual core Apple A6

GPU PowerVR SGX543MP3

Memory 1GB LPDDR2-1066 RAM

Storage 16 GB

Data Inputs Touch-screen

Display 4-inch (100 mm) diagonal (16:9 aspect ratio), multi-touch display,

640 × 1,136 pixels at 326 ppi, 800:1 contrast ratio (typical), 500

cd/m2 max. brightness (typical), Fingerprint-resistant oleophobic

coating on front

Rear Camera 8 MP back-side illuminated sensor, HD video (1080p)

Specifications IT4600r Scanner

Symbologies PDF417, MicroPDF417, MaxiCode, Data Matrix, QR

Code, Aztec, Aztec Mesas, Code 49, and EAN UCC

Composite

Interfaces All popular PCs and terminals via keyboard wedge,

keyboard replacement/direct connect, USB

Illumination LEDs 617nm ± 30nm

Aiming (Green LED

Aimer)

526nm ± 30nm

Image VGA, 752x480. Binary, TIFF, or JPEG output

Skew Angle & Pitch

Angle

±40o

Current Draw (Typical

RMS) Green LED Aimer

Input: 5 V, Scanning: 345mA, Idle: 80mA

Figure 1: Samsung Galaxy SII

Figure 2: iPhone 5

Figure 3: IT4600r Scanner

CONCLUSION AND FUTURE After working with these mobile scanners and seeing how they compared with a scanner you might find at a check out, we concluded that while the regular scanner may seem better than a smart phone, the smart phones performed better with higher success rates. There are instances that the barcode was scanned by the regular scanner successfully, the phones were unable to scan. The regular scanner scored a lot of 1-3’s but the phones scored more 0’s and 4-5’s more often than the 1-3’s. As technologies in optics improve, so will the efficiency of scanning barcodes.