# A Personal Microcomputer as an Access Control Management Platform in Road Transport
## 5 Analysis of the Results  
The system was developed for EU-standard licence plates. The tests were performed with 20 different paper licence test plates, of which 3 were confirmed and 17 were fake, created mainly for this purpose and stuck on the real plates. To check if the background colour impacts the OCR accuracy, three fake plates had a green background (simulating the EV's licence plates), and another three had a yellow background (simulating licence plates from countries like, e.g., The Netherlands). Three false numbers were chosen to make it especially hard to read by the OCR engine, including combinations of letters and digits that are very similar, like '0' and 'O', 'I' and '1'. The other numbers were chosen randomly from the possible European combinations. All tests were carried out on three different vehicles, differing in shape and colour. It seems not to have had any significant impact on the results. The tests were performed in different weather and lighting conditions. The regular illumination of the licence plate affects the chance of detecting the plate. All tests included 899 detections of the licence plates. Table 1 shows the results. The background colour of the cells in Table 1 corresponds to the background colour of each licence plate. The first three records are the actual plates. The overall success ratio is around 66%. Of 899 attempts, 591 ended up with correct recognition. The background's colour seems to have no significant impact on the result. As might have been expected, numbers with combinations of similar letters and digits were almost always recognised incorrectly. Randomly chosen numbers are usually identified correctly. However, to  
Figure 4: Raspberry Pi desktop running a help script. The program correctly determines licence plate position, recognises characters, and checks permission in the database. increase the value of these statistics, records with a small number of tries should be removed. Table 1 includes only records with 40 or more attempts. Table 1 contains two real and eight false plates, of which one is green and one is yellow. The coloured ones show a success ratio around average, which in this case is almost 71%. RBI04840 has the lowest success rate in the table above. It was the real plate with the little fatigued numbers. A similar case was found with RBIH096 in Table 2, although fewer trials existed. The correct recognisability coefficients of the remaining licence plates are identical and oscillate around the average. In most cases, the first recognition after the vehicle arrives is incorrect, but the second and subsequent ones are successful. Even lightning on the plate has a significant effect on the results. When the licence plate is illuminated only partially, the system usually does not detect the plate at all. However, the most problematic issue is the appearance of similar letters and digits in contiguous positions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Licence Plate** & \
\begin{tabular}{c} **Correct** \\ **Recognition** \\ \end{tabular} &
\begin{tabular}{c} **Incorrect** \\ **Recognition** \\ \end{tabular} & **Tries** & **Success Rate [\%]** \\ \hline RBI11156 & 25 & 15 & 40 & 62.50 \\ RBI04840 & 11 & 56 & 67 & 16.42 \\ RBI04840 & 11 & 56 & 67 & 16.42 \\ GWE72Y9 & 33 & 5 & 38 & 86.84 \\ WE984 KV & 79 & 24 & 103 & 76.70 \\ RBI11R7 & 17 & 4 & 21 & 80.95 \\ PO07 OOP & 0 & 25 & 25 & 0.00 \\ DW111B & 0 & 20 & 20 & 0.00 \\ PZI31I7 & 2 & 12 & 14 & 14.29 \\ GD241 AD & 36 & 13 & 49 & 73.47 \\ CB448 ES & 49 & 14 & 63 & 77.78 \\ KR776 JZ & 83 & 23 & 106 & 78.30 \\ ZS225 GY & 86 & 4 & 90 & 95.56 \\ DW375 UV & 47 & 13 & 60 & 78.33 \\ WWARH99 & 7 & 6 & 13 & 53.85 \\ GKA12I2 & 9 & 1 & 10 & 90.00 \\ NOLKT40 & 40 & 21 & 61 & 65.57 \\ KRK71U2 & 42 & 21 & 63 & 66.67 \\ TOSCB70 & 16 & 13 & 29 & 55.17 \\ BI446 FH & 5 & 2 & 7 & 71.43 \\ \hline  & 591 & 308 & 899 & 65.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of all plate detections.