Haar cascade resulting file is too small - opencv

Question

I am trying to train a cascade to detect an area with specifically structured text (MRZ).
I've gathered 200 positive samples and 572 negative samples.
Trainig went as the following:
opencv_traincascade.exe -data cascades -vec vector/vector.vec -bg bg.txt -numPos 200 -numNeg 572 -numStages 3 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -featureType LBP -mode ALL -w 400 -h 45 -maxFalseAlarmRate 0.8 -minHitRate 0.9988
PARAMETERS:
cascadeDirName: cascades
vecFileName: vector/vector.vec
bgFileName: bg.txt
numPos: 199
numNeg: 572 numStages: 3 precalcValBufSize[Mb] : 2048 precalcIdxBufSize[Mb] : 2048 acceptanceRatioBreakValue : -1 stageType: BOOST featureType: LBP sampleWidth: 400 sampleHeight: 45 boostType: GAB minHitRate: 0.9988 maxFalseAlarmRate: 0.8 weightTrimRate: 0.95 maxDepth: 1 maxWeakCount: 100 Number of unique features given windowSize [400,45] : 8778000
===== TRAINING 0-stage ===== <BEGIN POS count : consumed 199 : 199 NEG count : acceptanceRatio 572 : 1 Precalculation time: 26.994
+----+---------+---------+ | N | HR | FA |
+----+---------+---------+ | 1| 1| 1|
+----+---------+---------+ | 2| 1|0.0244755|
+----+---------+---------+ END>
Training until now has taken 0 days 0 hours 36 minutes 35 seconds.
===== TRAINING 1-stage ===== <BEGIN POS count : consumed 199 : 199 NEG count : acceptanceRatio
0 : 0 Required leaf false alarm rate achieved.
Branch training terminated.
The process was running ~35 minutes and produces a 2 kB file with only 45 lines that seems too small for a good cascade.
Needless to say, it doesn't detect the needed area.
I tried to tune the arguments but to no avail.
I know that it is better to use a larger set of samples, but I think that the result with this samples number should also produce a somewhat reasonable result, not so accurate though.
Is a haar cascade a good approach for detecting areas with specific text (MRZ)?
If so how better accuracy can be achieved?
Thanks in advance.

Answer 1

you want to produce 3 stages with maximum false alarm rate 0.8 per stage, this means after 3 stages the classifier will have a maximum of 0.8^3 false alarm rate = 0.512 but after your first stage, the classifier already reaches false alarm rate of 0.0244755 which is much better than your final aim (0.512) so the classifier is already good enough and does not need any more stages.
If that's not fine for you, increase numStages or decrease maxFalseAlarmRate to some amount that you don't reach the "final quality" within your first stage.
You will probably have to collect more samples and samples that represent the environment better, reaching such low false alarm rates is typically a sign for bad training data (too simple or too similar?).
I can't tell you, whether haar cascades are appropriate for solving your task.