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original
block
digits
output
original
block
digits
output
4.60 (.61)
3.20 (.14)
4.40 (.98)
3.20 (.48)
4.10 (.90)
4.60 (.52)
9.20 (.82)
4.60 (.93)
8.20 (.82)
4.60 (.96)
(a) (b)
Fig. 7.20. Problematic examples for combined meter value classifier. The original region
of interest (converted to grayscale), the preprocessed block along with the block classifier
output, the preprocessed digits for which digit recognition is queried along with the digit
classification, and the combined classifier output along with the recognized label and the
confidence are shown for: (a) some examples for which the person labeling the meter values
did not assign a valid label; (b) the five substitutions of the test set that are most difficult to
reject.
7.6 Conclusions
This chapter described a meter value recognition system that the author developed
in close cooperation with Siemens ElectroCom Postautomation GmbH. The system
consists of two stages: block recognition and digit recognition.
The block classifier, which is based on the Neural Abstraction Pyramid archi-
tecture, recognizes two digits of interest simultaneously within their context. It per-
forms significantly better than a flat neural classifier.
If block recognition cannot make a confident decision, the system focuses its
attention on the ambiguous digits by presenting them to a digit classifier. This digit
classifier has access to the block classification output for the other digit as context.
The system was evaluated using a database of Swedish Post meter values. The
combined system performs well. If the given region of interest contains a readable
meter value, it can be recognized with high accuracy. Even meter values challenging
for trained humans can be read.
The analysis of problematic test examples revealed that the recognition perfor-
mance could be improved further if the training set were larger, such that rare labels
were better represented, and if the region of interest contained only the digits of the
meter value and no additional objects.
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