13 3 . 1 mis2evt -computing eigenvector basis functions 1 3 3.2 mis2patl -generating patterns for the PNN classifier 15 3.3 hsfsyslrunning the updated version of the origmal NIST system 17 3.4 mis2pat2 -generating patterns for training the MLP classifier 19 3.5 trainregtraining to register a new form 3.6 hsfsys2 -rurming the new NIST recogniticm system 4. ALGORITHMIC OVERVIEW OF NEW SYSTEM HSFSYS2 4.1 The Application 4.2 System Components 4.2.1 Batch Initialization; src/lib/hsf/run.c; init_run0 25 4.2.2 Load Form Image; src/libAmage/readrast.c; ReadBinaryRasterO 4.2.3 Register Form Image; src/lib/hsf/regform.c; genregformSO 4.2.4 Remove Form Box; src/lib/rmlineA'emove.c; rm_long_hori_lineO 4.2.5 Isolate Line(s) of Handprint; src/lib/phrase/phrasm^.c; phrases_from_mapO 4.2.6 Segment Text Line(s); src/lib/adseg/segchars.c; blobs2chars80 4.2.7 Normalize Characters; src/lib/hsf/norm8.c; norm_2nd_gen_blobls80 3 1 4.2.8 Extract Feature Vectors; src/lib/im/ld.c; kl_transformO 32 4.2.9 Classify Characters; src/lib/mlp/runmlp.c; mlphypsconsO 4.2. 10 Spell-Correct Text Line(s); src/lib/phrase/spellphr.c; spell_phrases_Rel20 32 4.2.11 Store Results; src/lib/fet/writefet.c; writefetfileQ 5. PERFORMANCE EVALUATION AND COMPARISONS 5.1 Accuracies and Error Rates 5.2 Error versus Rejection Rate 5.3 Timing and Memory Statistics 6. IMPROVEMENTS TO THE TEST-BED 6.1 Processing New Forms with the HSFSYS2 7. FINAL COMMENTS 8. REFERENCES A. TRAINING THE MULTI-LAYER PERCEPTRON (MLP) CLASSIFIER OFF-LINE A.1 Training and Testing Runs 48 A.2 Specification (Spec) File A.2.1 String (Filename) Farms 49 A.2.2 Integer Farms A.2.3 Floating-Foint Farms 1 A.2.4 Switch Farms A.3 Training the MLF in hsfsys2 A.4 Explanation of the ou^ut produced dming MLF training A.4.
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