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Published byFanny Kusuma Modified over 6 years ago
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Self-supervised adaptation for on-line text recognition
Loïc Oudot, Lionel Prevost
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Overview Baseline system quick presentation Supervised adaptation
Self-supervised adaptation Semi-supervised adaptation Conclusions
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Writer independent baseline
Based on the activation-verification cognitive model of Paap Global encoding of the input stimulus Activation of a list of hypothetic word in lexicon Verification and selection of the best word candidate Data <x,y,p> Base line Segmentation Blank detector Shape Encoding Classifier Hypothetic Words list Lexicon Activation Probabilistic engine ASCII Verification More information on poster session (P34 October 27)
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Writer adaptation strategies
Causes of recognition rate reduction : Missing grapheme : the grapheme is missing in the prototype data set and must be stored (added) in this set Confusing grapheme : for a given writer, the prototype is confusing or erroneous and it must be removed from the prototype data set.
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Character classifier Prototype based classifier (1-nn)
Omni-writer strokes prototype data set 3 000 prototypes for 62 classes (lower-case, upper-case and digit) Computes a probabilistic vector of relevance for each classes Easy to add and remove prototypes
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Supervised adaptation
Comparison between the classification hypothesis and the label (segmentation known) Word error rate Data set size Words 50 100 150 Baseline system 25 % 100 % Text 1.3 % 1.1 % 0.6 % +6 % Line 0.7 % 0.4 % +4 % Good recognition rate Needs labeled data
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Self-supervised adaptation
Comparison between the classification hypothesis and the lexical hypothesis Systematical activation (SA) No needs of labeled data Small improvement of the recognition rate Word error rate Data set size Words 50 100 150 Baseline system 25 % 100 % SA 23 % +6 %
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Self-supervised adaptation
Conditional activation (CA) To limit the storage of bad prototypes we use the probabilistic lexical reliability (PLR) of a word : if a given word has a PLR > (threshold) the mis-classified characters are added in the prototype data set. Good recognition rate No needs of labeled data Growth of the prototype data set Word error rate Data set size Words 50 100 150 Baseline system 25 % 100 % CA 22% 20 % 17 % +2 %
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Self-supervised adaptation
Dynamic management Remove useless and erroneous prototypes : Each prototypes have an initial adequacy Q0 = 1000 C : Reward (+) the prototype i when it performs good classification I : Penalize (-) the prototype i when it performs mis-classification N : Penalize (-) for all the useless prototypes of the class j Qji(n+1) = Qji(n)+[C(n)-I(n)-N(n)]/Fj When Qji=0 the prototype is remove from the data set Huge reduction of the prototypes data base : -90 % Speed up of the recognition time Same recognition rate
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Adequacy evolution Omni-writer User prototypes
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Semi-supervised adaptation
Fusion between supervised and self-supervised adaptation First, we train the system in a supervised way with N words. Then, we use the dynamic management adaptation. N Recognition rate After enrolment After 100 words 70 % 76 % 10 74 % 83 % 20 88 % 30 90 % 50 75 % 91 % Best recognition rate : 99 % Worst recognition rate : 70 % Excellent recognition rate Few labeled data
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Conclusions We propose semi-supervised strategies with better results than supervised adaptation. Automatic reduction of a writer independent database into a writer-dependent database with great quality and compactness. For ambiguous words (<PLR<’), ask the writer to enter the label
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