## 5 Discussion  
In this research, we illustrated the application of DIR systems in education. We suggested employing an ensemble of DIR systems rather than creating a single DIR system to achieve this objective. We have devised two general ensemble-based DIR systems, namely VBDIR and SDBDIR. Within these ensembles, we incorporate various DIR systems that utilize diverse techniques. These include an OCR for textual content, spatial analysis of local regions, run-length histogram-based image features, or SURF features around words within the query document image. This approach is designed to retrieve the most relevant document images. Our findings, based on a dataset of university exam preparation books, demonstrate that ensemble DIR systems yield a higher mean average precision in DIR compared to each of the individual DIR systems within the ensemble. The results (Key-Based DIR: 0.5) reveal that among the weak DIR systems, the Key-Based DIR system delivers the highest performance in terms of the MAP metric. We utilized a widely used non-commercial OCR engine [35], which exhibited decreased performance, especially with blurred and skewed document images. Key-Based DIR outperforms OCR-Based DIR, particularly when dealing with images that contain more graphics than textual information, such as geometry questions. It is worth noting that Key-Based DIR may also be sensitive to blurred images, as its key point localization relies on edge extraction and contour computation. However, it is important to emphasize that the primary focus of this study is to demonstrate the superior performance of ensemble-based DIR systems compared to individual DIR systems. It is possible to enhance DIR systems' robustness against blur, rotation, or skewness by employing different key point detectors and feature extractors. The Word-Based DIR system demonstrates an ability to handle blurred images due to its focus on locating words rather than individual characters. However, it tends to exhibit poorer performance when document images contain more graphics than textual information. It is important to recognize that these three DIR systems--Key-Based DIR, OCR-Based DIR, and Word-Based DIR--possess distinct characteristics and advantages that make them suitable for different situations and challenges within DIR. Combining all the DIR systems into an SDBDIR ensemble yields an MAP of 0.82, as shown in Table 2. This demonstrates that leveraging different weak DIR systems with varying characteristics can significantly enhance search performance. Similar findings are observed with VBDIR. It is noteworthy that OCR + Word-Based and Key + Word-Based yield the same results in both VBDIR and SDBDIR, as they involve only a single weak retrieval. Interestingly, when OCR + Key-Based weak retrievals are used with Word-Based strong retrieval, VBDIR outperforms SDBDIR. These results suggest that the errors associated with strong retrievals can be mitigated by considering the decisions made by weak retrievals. In this investigation, SURF [27] characteristics were also employed to achieve rotational invariance. It is noteworthy that users utilizing the Tosbik [37] program typically \begin{table}
\begin{tabular}{c c} \hline \hline
**OCR + Word-Based** & **Key + Word-Based** \\ \hline
0.66 & 0.80 \\ \hline
**General image + Word-Based** & **OCR + Key + General image + Word-Based** \\ \hline
0.50 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: VBDIR ensemble retrievals’ MAP (SDBDIR). \begin{table}
\begin{tabular}{c c} \hline \hline
**OCR + Word-Based** & **Key + Word-Based** \\ \hline
0.66 & 0.80 \\ \hline
**General image + Word-Based** & **OCR + Key + General image + Word-Based** \\ \hline
0.50 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SDBDIR ensemble retrievals’ MAP (VBDIR). capture photographs in the correct orientation.