different table 0.001914242
space character 0.0017999119999999999
table row 0.001772604
table column 0.001752897
table recognition 0.001723841
table boundary 0.001710175
character type 0.001688253
special character 0.001682093
character transitions 0.001611209
character types 0.001593267
table characteristics 0.001582928
feature values 0.001574158
table columns 0.001564841
deterministic table 0.001556425
alphanumeric character 0.001552009
separator character 0.001550338
acter table 0.00154214
learning method 0.0015410670000000001
table recogni 0.001519575
table boundaries 0.001517347
typical table 0.001513568
table recog 0.001513163
space characters 0.001428616
feature extraction 0.001354078
special characters 0.0013107969999999998
character 0.0013104
following characters 0.001307791
learning algorithm 0.001305792
feature description 0.001302348
feature xtraction 0.001295624
table 0.00128641
training texts 0.001283696
punctuation characters 0.001252505
row information 0.0012258149999999999
learning approach 0.001213351
pace characters 0.0011820189999999999
surface features 0.001181989
separator characters 0.001179042
eparator characters 0.001165513
orspecial characters 0.001164988
symbolic features 0.001163679
numeric features 0.00116283
ascii characters 0.001162499
pecial characters 0.001162499
jacent characters 0.001162499
new text 0.00113828
first line 0.001127423
text category 0.001110646
learning parameters 0.001101561
first column 0.00109514
input text 0.001084432
large number 0.001068914
feature 0.00106071
average accuracy 0.001039499
machine learning 0.001034583
information extraction 0.001032989
recognition algorithm 0.001024754
different domain 0.0010233290000000001
learning algorithms 0.001022419
identification accuracy 0.001022254
free text 0.001020724
content information 0.001014767
text processing 0.001006236
header information 0.001000059
line line 9.9754E-4
plain text 9.94792E-4
training example 9.905320000000001E-4
new texts 9.83482E-4
put text 9.82568E-4
training examples 9.797830000000001E-4
possible values 9.74202E-4
title information 9.7059E-4
accuracy def 9.66427E-4
first hline 9.58882E-4
sample training 9.55898E-4
learning curve 9.545840000000001E-4
similar data 9.52882E-4
statistical data 9.49805E-4
texts figure 9.45115E-4
default learning 9.417830000000001E-4
connectionist learning 9.417830000000001E-4
characters 9.39104E-4
features 9.36867E-4
input texts 9.296339999999999E-4
each training 9.2816E-4
first vline 9.25822E-4
row recognition 9.23625E-4
same row 9.22275E-4
boundary space 9.13277E-4
tion algorithm 9.10121E-4
empirical results 9.09503E-4
ing approach 8.976979999999999E-4
last line 8.94392E-4
important data 8.85452E-4
new approach 8.80078E-4
appropriate number 8.77293E-4
new row 8.7139E-4
different domains 8.643710000000001E-4
last column 8.62109E-4
boundary recognition 8.61196E-4
