## 6 Results and Comparison  
The detailed results for each metric at each step in the workflow were documented across the population of 19 files. Averages calculated from the detailed results are shown in the graphs below.  
As can be seen in Figure 7, a moderate number of matches were made initially, but only 56.34% of those matches were correct. This is to be expected as a specific value could often satisfy a few of the regular expression patterns. The system had no intelligence about which one of the many values that match the regular expression for a key was the correct one and often simply selected the first one it could find after finding a key. After the introduction of index pairing, the precision grew considerably as not only more matches but also more correct matches were made. The increased number of matches was because the system now had the ability to read values above and below the key to find potential matches that would match the regular expression. The fact that there were more correct matches was because the irregularity that the OCR tools sometimes showed of placing a value above its key was overcome--the index pairing step addressed the instances where the OCR tool read lines out of order. A correct value could now be found for a key regardless of whether the key or value occurred first in the text. Finally, using ASM to find the keys in the files increased the precision further. Not many more  
Figure 6: Accuracy measurement points in workflow to show improvements as new functionality is introduced.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Calculation** \\ \hline Precision (P) & P = TP/(TP + FP) = TP/L \\ \hline Recall (R) & R = TP/(TP + FN) = TP/E \\ \hline F-Measure (F) & F = (2 x P x R)/(P + R) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculations for Precision, Recall and F-Measure ([PERSON] et al. [3]).  
matches were made, but the matches made were correct much more frequently. ASM thus assisted in overcoming the instances where OCR misread a word due to noise or document quality issues.  
In the raw OCR step the system identified 68.7% of the true matches. As can be seen in Figure 8, the introduction of index pairing (Step 2) increased the recall, showing that the system had a better ability to find the actual matches from the pool of possible matches. Again, this was due to the disorganized way in which the keys and values were read by OCR. Finally, the recall increased dramatically and almost all the true pairs were identified from the data text.  
As can be seen in Figure 9, the F-measure significantly increased after index pairing (Step 2) was introduced to the workflow and further significantly improved once ASM was added. The precision, recall and F-measure conclusions are well supported by the above graphs.  
Figure 10a shows a dramatic increase in the amount of true positive matches from the start point of the system to the end point. The matches made were increasingly more correct by overcoming the  
Figure 8: The average recall percentage at each of the three points of capturing the metrics.  
Figure 7: The average precision percentage at each of the three points of capturing the metrics.  
flaws of the OCR tools one by one--first by reorganizing the way the document output was read, and then by allowing some level of misread characters to be processed correctly. Just like the precision graph, the average shot up from the first step to the second, and then had another less dramatic increase. This trend in combination with the dramatic drop of false negative matches (as seen in Figure 10b) contribute to the overall shape of precision and recall.  
Figure 10: (**a**) The average true positive matches at each measuring point in the system, (**b**) the average of false negative matches at each measuring point in the system, and (**c**) the average of false positive matches at each measuring point in the system.  
Figure 9: The average F-measure percentage at each of the three points of capturing the metrics.