Of two inverted top right texts one gets scanned double, the upper one disappears

[rmast]

I'm investigating my issue earlier spotted in #3141 further.

In this picture above the text 'wis-clear' on the right, there is a text 'print'. This text print disappears completely and the text wis-clear has been read in twice.

---

Environment

• Tesseract Version: 5.2.0
• Commit Number: Added my own fix for ScrollView-debugging: Don't use std::unique_ptr for SVEvent #3870
• Platform: Linux Ubuntu20b 5.15.0-1014-azure # 17~20.04.1-Ubuntu SMP Thu Jun 23 20:01:51 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

Current Behavior:

Some inverted text on the top right disappears, other text gets scanned in twice.

There are two similar bounding boxes involved:

1. Processing word with lang Latin at:Bounding box=(2149,3103)->(2396,3144)
2. Processing word with lang Latin at:Bounding box=(2194,3114)->(2336,3137)

tesseract -c invert_threshold=0.9 --dpi 300 -l Latin -c textord_debug_tabfind=1 -c textord_debug_bugs=1 -c tessedit_reject_block_percent=1 -c tessedit_reject_row_percent=1 -c debug_noise_removal=1 -c textord_debug_block=28 -c textord_tabfind_show_partitions=1 -c textord_tabfind_show_initial_partitions=1 -c textord_tabfind_show_reject_blobs=1 -c textord_tabfind_show_blocks=1 -c textord_show_final_rows=1 -c textord_show_final_blobs=1 -c textord_show_initial_rows=1 -c textord_debug_blob=1 -c textord_oldbl_debug=1 -c textord_debug_baselines=1 -c textord_show_tables=1 -c textord_test_mode=1 -c classify_debug_level=1 -c dawg_debug_level=1 -c wordrec_debug_level=1 -c segsearch_debug_level=1 -c wordrec_display_segmentations=1 -c bidi_debug=1 -c debug_noise_removal=1 -c paragraph_debug_level=1 175789293-f39ddfdb-6f3e-4598-8d16-80a1f4a88b36.jpg output89



Processing word with lang Latin at:Bounding box=(2149,3103)->(2396,3144)
Trying word using lang Latin, oem 1
Inverting image: old min=0.19685, mean=0.19685, sd=0, inv 0.228346,0.826547,0.197407
<null>=301 On [0, 2), scores= 98.8(I=6=0.102) 99.2(|=61=0.26), Mean=98.9874, max=99.209
|=61 On [2, 7), scores= 19.8(<null>=301=50.9) 19.8([=55=22.9) 0.0475(<null>=301=79.1) 5.46e-06(<null>=301=95.3) 1.85e-07(<null>=301=98.3), Mean=7.9283, max=19.8422
>=132 On [7, 12), scores= 88.4(<null>=301=7.39) 98.7()=8=0.725) 39.6(<null>=301=60.4) 0.00681(<null>=301=100) 2.44e-05(<null>=301=99.8), Mean=45.3386, max=98.7152
 =0 On [12, 17), scores= 71.6(<null>=301=28.3) 99.6(<null>=301=0.327) 85(<null>=301=14.9) 0.619(<null>=301=99.4) 0.000118(<null>=301=99.8), Mean=51.3589, max=99.5623
w=89 On [17, 22), scores= 80.5(<null>=301=19.5) 100(<null>=301=0.0252) 87.4(<null>=301=12.6) 0.0225(<null>=301=100) 1.3e-06(<null>=301=99.9), Mean=53.5728, max=99.9746
i=78 On [22, 26), scores= 75.7(<null>=301=24.3) 100(í=153=0.0154) 51.1(<null>=301=48.9) 0.000347(<null>=301=100), Mean=56.6989, max=99.9755
s=74 On [26, 33), scores= 6.01(<null>=301=94) 98.4(<null>=301=1.64) 99.9(<null>=301=0.0488) 52.9(<null>=301=47.1) 0.000503(<null>=301=100) 0.000516(<null>=301=98.2) 0.000678(<null>=301=83.4), Mean=36.7451, max=99.9412
-=14 On [33, 40), scores= 22.1(<null>=301=70.7) 94.5(<null>=301=5.25) 99.5(<null>=301=0.327) 27.5(<null>=301=72.5) 0.0107(<null>=301=100) 0.00745(<null>=301=93.4) 0.0476(<null>=301=92.3), Mean=34.808, max=99.4556
c=86 On [40, 46), scores= 66.9(<null>=301=30.3) 96.5(<null>=301=2.68) 97.2(<null>=301=1.46) 10.9(<null>=301=89) 0.000122(<null>=301=100) 1.28e-05(<null>=301=91.2), Mean=45.2322, max=97.1531
l=84 On [46, 50), scores= 90.1(<null>=301=8.78) 98.7(I=6=0.467) 47.2(<null>=301=52.8) 0.0014(<null>=301=100), Mean=59.004, max=98.7325
e=73 On [50, 56), scores= 12.1(<null>=301=87.9) 96.4(<null>=301=3.57) 99.6(<null>=301=0.156) 45.2(<null>=301=54.8) 1.23e-06(<null>=301=100) 1.46e-06(<null>=301=99), Mean=42.2193, max=99.6454
a=82 On [56, 61), scores= 91.5(<null>=301=8.5) 99.9(<null>=301=0.0377) 61.3(<null>=301=38.7) 3.06e-06(<null>=301=100) 5.04e-08(<null>=301=95.5), Mean=50.5396, max=99.9351
r=68 On [61, 67), scores= 90.1(<null>=301=9.85) 99.9(<null>=301=0.0692) 89.4(<null>=301=10.6) 0.123(<null>=301=99.9) 0.00747(<null>=301=99.7) 0.0617(<null>=301=86.8), Mean=46.5988, max=99.8875
 =0 On [67, 75), scores= 52.2(<null>=301=40.9) 61.3(<null>=301=30.5) 37.8(<null>=301=44.4) 17.2(<null>=301=56.1) 4.55(<null>=301=60.1) 4.55(<null>=301=72.9) 3.99(<null>=301=86.8) 7.51(<null>=301=86.3), Mean=23.6451, max=61.3004
 =0 On [75, 79), scores= 28.3(<null>=301=65.7) 47(<null>=301=45.8) 20.1(<null>=301=77.5) 2.62(<null>=301=94.2), Mean=24.5262, max=47.0312
|=61 On [79, 83), scores= 65.5(<null>=301=33.8) 99.8(]=50=0.0972) 5.8(<null>=301=94.1) 1.23e-06(<null>=301=100), Mean=42.7894, max=99.8109
0 null_char score=-0.0974199, c=-0.0974199, perm=0, hash=0
1 null_char score=-0.190361, c=-0.0929412, perm=0, hash=0 prev:null_char score=-0.0974199, c=-0.0974199, perm=0, hash=0
2 label=61, uid=63=| [7c ] score=-0.620865, c=-0.430504, Start End perm=8, hash=3d prev:null_char score=-0.190361, c=-0.0929412, perm=0, hash=0
3 label=61, uid=63=| [7c ] score=-2.32779, c=-1.70693, perm=8, hash=3d prev:label=61, uid=63=| [7c ] score=-0.620865, c=-0.430504, Start End perm=8, hash=3d
4 null_char score=-2.64755, c=-0.31976, perm=0, hash=3d prev:label=61, uid=63=| [7c ] score=-2.32779, c=-1.70693, perm=8, hash=3d
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7 label=132, uid=134=> [3e ] score=-3.0103, c=-0.12795, End perm=8, hash=487a prev:null_char score=-2.88235, c=-0.102121, perm=0, hash=3d
8 label=132, uid=134=> [3e ] score=-3.10823, c=-0.0979313, perm=8, hash=487a prev:label=132, uid=134=> [3e ] score=-3.0103, c=-0.12795, End perm=8, hash=487a
9 label=132, uid=134=> [3e ] score=-3.19386, c=-0.0856231, perm=8, hash=487a prev:label=132, uid=134=> [3e ] score=-3.10823, c=-0.0979313, perm=8, hash=487a
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12 label=0, uid=0=  [20 ] score=-3.45184, c=-0.0861606, DawgStart perm=8, hash=557fec prev:null_char score=-3.36568, c=-0.0867499, perm=0, hash=487a
13 label=0, uid=0=  [20 ] score=-3.54123, c=-0.0893863, perm=8, hash=557fec prev:label=0, uid=0=  [20 ] score=-3.45184, c=-0.0861606, DawgStart perm=8, hash=557fec
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17 label=89, uid=91=w [77 ]a score=-3.89015, c=-0.0850372, Start End perm=8, hash=64dce8c1 prev:null_char score=-3.80511, c=-0.0865476, perm=0, hash=557fec
18 label=89, uid=91=w [77 ]a score=-3.9754, c=-0.0852538, perm=8, hash=64dce8c1 prev:label=89, uid=91=w [77 ]a score=-3.89015, c=-0.0850372, Start End perm=8, hash=64dce8c1
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20 null_char score=-4.14566, c=-0.0852257, perm=0, hash=64dce8c1 prev:label=89, uid=91=w [77 ]a score=-4.06044, c=-0.0850329, perm=8, hash=64dce8c1
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22 label=78, uid=80=i [69 ]a score=-4.31672, c=-0.0850103, End perm=8, hash=76fc9a93fc prev:null_char score=-4.23171, c=-0.0860455, perm=0, hash=64dce8c1
23 label=78, uid=80=i [69 ]a score=-4.40196, c=-0.0852454, perm=8, hash=76fc9a93fc prev:label=78, uid=80=i [69 ]a score=-4.31672, c=-0.0850103, End perm=8, hash=76fc9a93fc
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26 label=74, uid=76=s [73 ]a score=-4.65701, c=-0.0850182, End perm=8, hash=8c5dfe5a9392 prev:null_char score=-4.57199, c=-0.0850036, perm=0, hash=76fc9a93fc
27 label=74, uid=76=s [73 ]a score=-4.75855, c=-0.101541, perm=8, hash=8c5dfe5a9392 prev:label=74, uid=76=s [73 ]a score=-4.65701, c=-0.0850182, End perm=8, hash=8c5dfe5a9392
28 label=74, uid=76=s [73 ]a score=-4.84414, c=-0.0855878, perm=8, hash=8c5dfe5a9392 prev:label=74, uid=76=s [73 ]a score=-4.75855, c=-0.101541, perm=8, hash=8c5dfe5a9392
29 label=74, uid=76=s [73 ]a score=-4.92929, c=-0.0851501, perm=8, hash=8c5dfe5a9392 prev:label=74, uid=76=s [73 ]a score=-4.84414, c=-0.0855878, perm=8, hash=8c5dfe5a9392
30 null_char score=-5.0143, c=-0.0850075, perm=0, hash=8c5dfe5a9392 prev:label=74, uid=76=s [73 ]a score=-4.92929, c=-0.0851501, perm=8, hash=8c5dfe5a9392
31 label=0, uid=0=  [20 ] score=-5.09956, c=-0.0852623, DawgStart perm=8, hash=a596e20eda163c prev:null_char score=-5.0143, c=-0.0850075, perm=0, hash=8c5dfe5a9392
32 label=0, uid=0=  [20 ] score=-7.03714, c=-1.93758, perm=8, hash=a596e20eda163c prev:label=0, uid=0=  [20 ] score=-5.09956, c=-0.0852623, DawgStart perm=8, hash=a596e20eda163c
33 label=14, uid=16=- [2d ]p score=-7.19661, c=-0.159473, Start perm=1, hash=c357fead85463ad6 prev:label=0, uid=0=  [20 ] score=-7.03714, c=-1.93758, perm=8, hash=a596e20eda163c
34 label=14, uid=16=- [2d ]p score=-7.33845, c=-0.141836, perm=1, hash=c357fead85463ad6 prev:label=14, uid=16=- [2d ]p score=-7.19661, c=-0.159473, Start perm=1, hash=c357fead85463ad6
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36 label=14, uid=16=- [2d ]p score=-7.51424, c=-0.0853334, perm=1, hash=c357fead85463ad6 prev:label=14, uid=16=- [2d ]p score=-7.42891, c=-0.0904584, perm=1, hash=c357fead85463ad6
37 null_char score=-7.59962, c=-0.0853829, perm=0, hash=c357fead85463ad6 prev:label=14, uid=16=- [2d ]p score=-7.51424, c=-0.0853334, perm=1, hash=c357fead85463ad6
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41 label=86, uid=88=c [63 ]a score=-8.15245, c=-0.120332, perm=8, hash=71ce70b338d969b0 prev:label=86, uid=88=c [63 ]a score=-8.03211, c=-0.113906, End perm=8, hash=71ce70b338d969b0
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46 label=84, uid=86=l [6c ]a score=-8.71158, c=-0.0966929, End perm=8, hash=4188f36d107aae7a prev:null_char score=-8.61489, c=-0.176952, perm=0, hash=71ce70b338d969b0
47 label=84, uid=86=l [6c ]a score=-8.80934, c=-0.0977558, perm=8, hash=4188f36d107aae7a prev:label=84, uid=86=l [6c ]a score=-8.71158, c=-0.0966929, End perm=8, hash=4188f36d107aae7a
48 label=84, uid=86=l [6c ]a score=-8.89453, c=-0.0851927, perm=8, hash=4188f36d107aae7a prev:label=84, uid=86=l [6c ]a score=-8.80934, c=-0.0977558, perm=8, hash=4188f36d107aae7a
49 null_char score=-8.97964, c=-0.0851056, perm=0, hash=4188f36d107aae7a prev:label=84, uid=86=l [6c ]a score=-8.89453, c=-0.0851927, perm=8, hash=4188f36d107aae7a
50 label=73, uid=75=e [65 ]a score=-9.06464, c=-0.0850046, End perm=8, hash=4f8f2aa970b9d482 prev:null_char score=-8.97964, c=-0.0851056, perm=0, hash=4188f36d107aae7a
51 label=73, uid=75=e [65 ]a score=-9.18636, c=-0.121723, perm=8, hash=4f8f2aa970b9d482 prev:label=73, uid=75=e [65 ]a score=-9.06464, c=-0.0850046, End perm=8, hash=4f8f2aa970b9d482
52 label=73, uid=75=e [65 ]a score=-9.27491, c=-0.0885518, perm=8, hash=4f8f2aa970b9d482 prev:label=73, uid=75=e [65 ]a score=-9.18636, c=-0.121723, perm=8, hash=4f8f2aa970b9d482
53 label=73, uid=75=e [65 ]a score=-9.36019, c=-0.0852759, perm=8, hash=4f8f2aa970b9d482 prev:label=73, uid=75=e [65 ]a score=-9.27491, c=-0.0885518, perm=8, hash=4f8f2aa970b9d482
54 null_char score=-9.4452, c=-0.0850073, perm=0, hash=4f8f2aa970b9d482 prev:label=73, uid=75=e [65 ]a score=-9.36019, c=-0.0852759, perm=8, hash=4f8f2aa970b9d482
55 null_char score=-9.54014, c=-0.0949398, perm=0, hash=4f8f2aa970b9d482 prev:null_char score=-9.4452, c=-0.0850073, perm=0, hash=4f8f2aa970b9d482
56 label=82, uid=84=a [61 ]a score=-9.62516, c=-0.0850175, End perm=8, hash=dae453e2fb38b20b prev:null_char score=-9.54014, c=-0.0949398, perm=0, hash=4f8f2aa970b9d482
57 label=82, uid=84=a [61 ]a score=-9.7108, c=-0.0856489, perm=8, hash=dae453e2fb38b20b prev:label=82, uid=84=a [61 ]a score=-9.62516, c=-0.0850175, End perm=8, hash=dae453e2fb38b20b
58 label=82, uid=84=a [61 ]a score=-9.79602, c=-0.0852179, perm=8, hash=dae453e2fb38b20b prev:label=82, uid=84=a [61 ]a score=-9.7108, c=-0.0856489, perm=8, hash=dae453e2fb38b20b
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67 label=0, uid=0=  [20 ] score=-10.826, c=-0.157074, DawgStart perm=8, hash=a94deda592a817c3 prev:null_char score=-10.6689, c=-0.226327, perm=0, hash=395af5c45ce20a40
68 label=0, uid=0=  [20 ] score=-11.4004, c=-0.574383, perm=8, hash=a94deda592a817c3 prev:label=0, uid=0=  [20 ] score=-10.826, c=-0.157074, DawgStart perm=8, hash=a94deda592a817c3
69 label=0, uid=0=  [20 ] score=-11.6816, c=-0.281208, perm=8, hash=a94deda592a817c3 prev:label=0, uid=0=  [20 ] score=-11.4004, c=-0.574383, perm=8, hash=a94deda592a817c3
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75 null_char score=-14.3032, c=-0.504484, perm=0, hash=a94deda592a817c3 prev:null_char score=-13.7988, c=-0.232681, perm=0, hash=a94deda592a817c3
76 null_char score=-15.169, c=-0.865767, perm=0, hash=a94deda592a817c3 prev:null_char score=-14.3032, c=-0.504484, perm=0, hash=a94deda592a817c3
77 null_char score=-15.5085, c=-0.339494, perm=0, hash=a94deda592a817c3 prev:null_char score=-15.169, c=-0.865767, perm=0, hash=a94deda592a817c3
78 null_char score=-15.6532, c=-0.144672, perm=0, hash=a94deda592a817c3 prev:null_char score=-15.5085, c=-0.339494, perm=0, hash=a94deda592a817c3
79 label=61, uid=63=| [7c ] score=-15.7448, c=-0.0916148, Start End perm=8, hash=b9ee5953024c090e prev:null_char score=-15.6532, c=-0.144672, perm=0, hash=a94deda592a817c3
80 label=61, uid=63=| [7c ] score=-15.8317, c=-0.0868923, perm=8, hash=b9ee5953024c090e prev:label=61, uid=63=| [7c ] score=-15.7448, c=-0.0916148, Start End perm=8, hash=b9ee5953024c090e
81 label=61, uid=63=| [7c ] score=-15.9172, c=-0.085523, perm=8, hash=b9ee5953024c090e prev:label=61, uid=63=| [7c ] score=-15.8317, c=-0.0868923, perm=8, hash=b9ee5953024c090e
82 null_char score=-16.0022, c=-0.0850035, perm=0, hash=b9ee5953024c090e prev:label=61, uid=63=| [7c ] score=-15.9172, c=-0.085523, perm=8, hash=b9ee5953024c090e

Second choice path:
2 63=| [7c ] r=2.32779, c=-1.70693, s=1, e=1, perm=8
7 134=> [3e ] r=1.03789, c=-0.31976, s=0, e=1, perm=8
12 0=  [20 ] r=0.261591, c=-0.0893863, s=0, e=0, perm=8
17 91=w [77 ]a r=0.433167, c=-0.0912952, s=1, e=1, perm=8
22 80=i [69 ]a r=0.426551, c=-0.0860455, s=0, e=1, perm=8
26 76=s [73 ]a r=0.527308, c=-0.101541, s=0, e=1, perm=8
31 0=  [20 ] r=2.02284, c=-1.93758, s=0, e=0, perm=8
33 16=- [2d ]p r=0.477101, c=-0.159473, s=1, e=0, perm=1
40 88=c [63 ]a r=0.838686, c=-0.165152, s=0, e=1, perm=8
46 86=l [6c ]a r=0.541606, c=-0.176952, s=0, e=1, perm=8
50 75=e [65 ]a r=0.465661, c=-0.121723, s=0, e=1, perm=8
56 84=a [61 ]a r=0.435831, c=-0.0949398, s=0, e=1, perm=8
61 70=r [72 ]a r=6.64548, c=-1.27292, s=0, e=1, perm=8
76 0=  [20 ] r=1.08423, c=-0.83936, s=0, e=0, perm=8
79 63=| [7c ] r=1.11084, c=-0.325512, s=0, e=0, perm=2
Path total rating = 18.6366
2 63=| [7c ] r=2.32779, c=-1.70693, s=1, e=1, perm=8
7 134=> [3e ] r=1.03789, c=-0.31976, s=0, e=1, perm=8
12 0=  [20 ] r=0.261591, c=-0.0893863, s=0, e=0, perm=8
17 91=w [77 ]a r=0.433167, c=-0.0912952, s=1, e=1, perm=8
22 80=i [69 ]a r=0.426551, c=-0.0860455, s=0, e=1, perm=8
26 76=s [73 ]a r=0.527308, c=-0.101541, s=0, e=1, perm=8
31 0=  [20 ] r=2.02284, c=-1.93758, s=0, e=0, perm=8
33 16=- [2d ]p r=0.477101, c=-0.159473, s=1, e=0, perm=1
40 88=c [63 ]a r=0.838686, c=-0.165152, s=0, e=1, perm=8
46 86=l [6c ]a r=0.541606, c=-0.176952, s=0, e=1, perm=8
50 75=e [65 ]a r=0.465661, c=-0.121723, s=0, e=1, perm=8
56 84=a [61 ]a r=0.435831, c=-0.0949398, s=0, e=1, perm=8
61 70=r [72 ]a r=0.872893, c=-0.226327, s=0, e=1, perm=8
67 0=  [20 ] r=1.01267, c=-0.574383, s=0, e=0, perm=8
79 63=| [7c ] r=4.32063, c=-0.865767, s=1, e=1, perm=8
Path total rating = 16.0022
Best choice: accepted=0, adaptable=0, done=1 : Lang result : |> : R=3.36568, C=-11.9485, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM    NORM
str     |       >
state:  1       1
C       -1.707  -0.320
Best choice: accepted=0, adaptable=0, done=1 : Lang result : wis : R=1.38703, C=-13.5631, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM    NORM    NORM
str     w       i       s
state:  1       1       1
C       -0.091  -0.086  -0.102
Best choice: accepted=0, adaptable=0, done=1 : Lang result : -clear : R=3.63178, C=-13.5631, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM    NORM    NORM    NORM    NORM    NORM
str     -       c       l       e       a       r
state:  1       1       1       1       1       1
C       -0.159  -0.165  -0.177  -0.122  -0.095  -0.226
Best choice: accepted=0, adaptable=0, done=1 : Lang result : | : R=4.32063, C=-6.06037, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM
str     |
state:  1
C       -0.866
1 new words better than 0 old words: r: 3.36568 v 0 c: -11.9485 v 0 valid dict: 1 v 0
1 new words better than 0 old words: r: 1.38703 v 0 c: -13.5631 v 0 valid dict: 1 v 0
1 new words better than 0 old words: r: 3.63178 v 0 c: -13.5631 v 0 valid dict: 1 v 0
1 new words better than 0 old words: r: 4.32063 v 0 c: -6.06037 v 0 valid dict: 1 v 0
Pass1: | [| [7c ] ]
Processing word with lang Latin at:Bounding box=(2194,3114)->(2336,3137)
Trying word using lang Latin, oem 1
Inverting image: old min=0.133858, mean=0.137795, sd=0.00393701, inv 0.598425,0.91916,0.115633
<null>=301 On [0, 2), scores= 100(a=82=0.00099) 100(w=89=4.14e-05), Mean=99.9961, max=99.9999
w=89 On [2, 8), scores= 4.35(<null>=301=95.6) 99.2(<null>=301=0.827) 100(<null>=301=0.00486) 99.5(<null>=301=0.479) 2.57(<null>=301=97.4) 1.74e-09(<null>=301=100), Mean=50.9318, max=99.9945
i=78 On [8, 14), scores= 15.8(<null>=301=84.2) 99.8(<null>=301=0.18) 100(I=6=0.00295) 59.9(<null>=301=40.1) 0.00117(<null>=301=100) 4.78e-05(<null>=301=100), Mean=45.9054, max=99.9933
s=74 On [14, 19), scores= 65.3(<null>=301=34.6) 100(S=3=0.00204) 99.8(<null>=301=0.158) 14(<null>=301=85.9) 4.58e-07(<null>=301=100), Mean=55.8452, max=99.9955
 =0 On [19, 25), scores= 6.64(<null>=301=93.4) 99.1(<null>=301=0.856) 100(<null>=301=0.0121) 75.5(<null>=301=24.5) 0.0372(<null>=301=100) 3.23e-05(<null>=301=100), Mean=46.8835, max=99.9856
-=14 On [25, 30), scores= 48(<null>=301=51.9) 97.7(-=291=0.532) 84.5(<null>=301=15.4) 0.274(<null>=301=99.7) 1.61e-05(<null>=301=100), Mean=46.0863, max=97.6613
 =0 On [30, 36), scores= 42.1(<null>=301=57.9) 99.7(<null>=301=0.325) 100(<null>=301=0.0303) 39.6(<null>=301=60.4) 0.00343(<null>=301=100) 0.00507(<null>=301=99.3), Mean=46.899, max=99.969
c=86 On [36, 41), scores= 85.2(<null>=301=14.8) 99.9(e=73=0.0795) 84.5(<null>=301=15.5) 0.163(<null>=301=99.8) 1.79e-08(<null>=301=100), Mean=53.9375, max=99.8645
l=84 On [41, 47), scores= 17.3(<null>=301=82.7) 97.6(<null>=301=2.25) 99.3(<null>=301=0.665) 47.2(<null>=301=52.8) 0.000567(<null>=301=100) 7.29e-08(<null>=301=100), Mean=43.5683, max=99.2975
e=73 On [47, 53), scores= 75.5(<null>=301=24.5) 100(<null>=301=0.0134) 99.9(<null>=301=0.0646) 27.5(<null>=301=72.5) 1.67e-06(<null>=301=100) 1.85e-07(<null>=301=99.9), Mean=50.4882, max=99.9856
a=82 On [53, 59), scores= 73.5(<null>=301=26.5) 100(<null>=301=0.00326) 99.9(<null>=301=0.123) 8.94(<null>=301=91.1) 3.76e-08(<null>=301=100) 9.61e-09(<null>=301=98.7), Mean=47.0509, max=99.9966
r=68 On [59, 65), scores= 97.5(<null>=301=2.53) 100(<null>=301=0.00341) 81.3(<null>=301=18.7) 0.117(<null>=301=99.9) 5.71e-05(<null>=301=100) 1.01e-07(<null>=301=100), Mean=46.4862, max=99.9957
0 null_char score=-0.0850772, c=-0.0850772, perm=0, hash=0
1 null_char score=-0.170079, c=-0.0850014, perm=0, hash=0 prev:null_char score=-0.0850772, c=-0.0850772, perm=0, hash=0
2 label=89, uid=91=w [77 ]a score=-0.255138, c=-0.0850592, Start End perm=8, hash=59 prev:null_char score=-0.170079, c=-0.0850014, perm=0, hash=0
3 label=89, uid=91=w [77 ]a score=-0.348624, c=-0.0934861, perm=8, hash=59 prev:label=89, uid=91=w [77 ]a score=-0.255138, c=-0.0850592, Start End perm=8, hash=59
4 label=89, uid=91=w [77 ]a score=-0.433679, c=-0.0850552, perm=8, hash=59 prev:label=89, uid=91=w [77 ]a score=-0.348624, c=-0.0934861, perm=8, hash=59
5 label=89, uid=91=w [77 ]a score=-0.523489, c=-0.0898102, perm=8, hash=59 prev:label=89, uid=91=w [77 ]a score=-0.433679, c=-0.0850552, perm=8, hash=59
6 label=89, uid=91=w [77 ]a score=-0.608491, c=-0.0850013, perm=8, hash=59 prev:label=89, uid=91=w [77 ]a score=-0.523489, c=-0.0898102, perm=8, hash=59
7 null_char score=-0.693491, c=-0.085, perm=0, hash=59 prev:label=89, uid=91=w [77 ]a score=-0.608491, c=-0.0850013, perm=8, hash=59
8 label=78, uid=80=i [69 ]a score=-0.778492, c=-0.0850016, End perm=8, hash=694c prev:null_char score=-0.693491, c=-0.085, perm=0, hash=59
9 label=78, uid=80=i [69 ]a score=-0.865298, c=-0.0868062, perm=8, hash=694c prev:label=78, uid=80=i [69 ]a score=-0.778492, c=-0.0850016, End perm=8, hash=694c
10 label=78, uid=80=i [69 ]a score=-0.950366, c=-0.0850674, perm=8, hash=694c prev:label=78, uid=80=i [69 ]a score=-0.865298, c=-0.0868062, perm=8, hash=694c
11 label=78, uid=80=i [69 ]a score=-1.03538, c=-0.0850176, perm=8, hash=694c prev:label=78, uid=80=i [69 ]a score=-0.950366, c=-0.0850674, perm=8, hash=694c
12 null_char score=-1.1204, c=-0.0850117, perm=0, hash=694c prev:label=78, uid=80=i [69 ]a score=-1.03538, c=-0.0850176, perm=8, hash=694c
13 null_char score=-1.20545, c=-0.0850533, perm=0, hash=694c prev:null_char score=-1.1204, c=-0.0850117, perm=0, hash=694c
14 label=74, uid=76=s [73 ]a score=-1.29065, c=-0.0851989, End perm=8, hash=7c37f2 prev:null_char score=-1.20545, c=-0.0850533, perm=0, hash=694c
15 label=74, uid=76=s [73 ]a score=-1.37569, c=-0.0850446, perm=8, hash=7c37f2 prev:label=74, uid=76=s [73 ]a score=-1.29065, c=-0.0851989, End perm=8, hash=7c37f2
16 label=74, uid=76=s [73 ]a score=-1.46234, c=-0.0866529, perm=8, hash=7c37f2 prev:label=74, uid=76=s [73 ]a score=-1.37569, c=-0.0850446, perm=8, hash=7c37f2
17 label=74, uid=76=s [73 ]a score=-1.54738, c=-0.0850319, perm=8, hash=7c37f2 prev:label=74, uid=76=s [73 ]a score=-1.46234, c=-0.0866529, perm=8, hash=7c37f2
18 null_char score=-1.63238, c=-0.085, perm=0, hash=7c37f2 prev:label=74, uid=76=s [73 ]a score=-1.54738, c=-0.0850319, perm=8, hash=7c37f2
19 null_char score=-1.78606, c=-0.153684, perm=0, hash=7c37f2 prev:null_char score=-1.63238, c=-0.085, perm=0, hash=7c37f2
20 label=0, uid=0=  [20 ] score=-1.87108, c=-0.0850225, perm=8, hash=9289ff7c prev:null_char score=-1.78606, c=-0.153684, perm=0, hash=7c37f2
21 label=0, uid=0=  [20 ] score=-2.06266, c=-0.191574, perm=8, hash=9289ff7c prev:label=0, uid=0=  [20 ] score=-1.87108, c=-0.0850225, perm=8, hash=9289ff7c
22 label=0, uid=0=  [20 ] score=-2.25419, c=-0.191534, perm=8, hash=9289ff7c prev:label=0, uid=0=  [20 ] score=-2.06266, c=-0.191574, perm=8, hash=9289ff7c
23 null_char score=-2.44628, c=-0.192088, perm=2, hash=9289ff7c prev:label=0, uid=0=  [20 ] score=-2.25419, c=-0.191534, perm=8, hash=9289ff7c
24 null_char score=-2.63764, c=-0.191357, perm=2, hash=9289ff7c prev:null_char score=-2.44628, c=-0.192088, perm=2, hash=9289ff7c
25 label=14, uid=16=- [2d ]p score=-2.83096, c=-0.193324, perm=2, hash=acdecb6456 prev:null_char score=-2.63764, c=-0.191357, perm=2, hash=9289ff7c
26 label=14, uid=16=- [2d ]p score=-3.07546, c=-0.244496, perm=2, hash=acdecb6456 prev:label=14, uid=16=- [2d ]p score=-2.83096, c=-0.193324, perm=2, hash=acdecb6456
27 label=14, uid=16=- [2d ]p score=-3.27022, c=-0.194767, perm=2, hash=acdecb6456 prev:label=14, uid=16=- [2d ]p score=-3.07546, c=-0.244496, perm=2, hash=acdecb6456
28 null_char score=-3.4677, c=-0.197473, perm=2, hash=acdecb6456 prev:label=14, uid=16=- [2d ]p score=-3.27022, c=-0.194767, perm=2, hash=acdecb6456
29 null_char score=-3.65897, c=-0.19127, perm=2, hash=acdecb6456 prev:null_char score=-3.4677, c=-0.197473, perm=2, hash=acdecb6456
30 label=0, uid=0=  [20 ] score=-3.74404, c=-0.0850688, DawgStart perm=0, hash=cbeed3f05d74 prev:null_char score=-3.65897, c=-0.19127, perm=2, hash=acdecb6456
31 label=0, uid=0=  [20 ] score=-3.83231, c=-0.0882736, perm=0, hash=cbeed3f05d74 prev:label=0, uid=0=  [20 ] score=-3.74404, c=-0.0850688, DawgStart perm=0, hash=cbeed3f05d74
32 label=0, uid=0=  [20 ] score=-3.91762, c=-0.08531, perm=0, hash=cbeed3f05d74 prev:label=0, uid=0=  [20 ] score=-3.83231, c=-0.0882736, perm=0, hash=cbeed3f05d74
33 label=0, uid=0=  [20 ] score=-4.00262, c=-0.085002, perm=0, hash=cbeed3f05d74 prev:label=0, uid=0=  [20 ] score=-3.91762, c=-0.08531, perm=0, hash=cbeed3f05d74
34 null_char score=-4.08766, c=-0.0850354, perm=0, hash=cbeed3f05d74 prev:label=0, uid=0=  [20 ] score=-4.00262, c=-0.085002, perm=0, hash=cbeed3f05d74
35 null_char score=-4.17984, c=-0.0921804, perm=0, hash=cbeed3f05d74 prev:null_char score=-4.08766, c=-0.0850354, perm=0, hash=cbeed3f05d74
36 label=86, uid=88=c [63 ]a score=-4.26509, c=-0.0852522, Start End perm=8, hash=f093be058e3f2e prev:null_char score=-4.17984, c=-0.0921804, perm=0, hash=cbeed3f05d74
37 label=86, uid=88=c [63 ]a score=-4.35145, c=-0.0863558, perm=8, hash=f093be058e3f2e prev:label=86, uid=88=c [63 ]a score=-4.26509, c=-0.0852522, Start End perm=8, hash=f093be058e3f2e
38 label=86, uid=88=c [63 ]a score=-4.43671, c=-0.085263, perm=8, hash=f093be058e3f2e prev:label=86, uid=88=c [63 ]a score=-4.35145, c=-0.0863558, perm=8, hash=f093be058e3f2e
39 null_char score=-4.52338, c=-0.0866698, perm=0, hash=f093be058e3f2e prev:label=86, uid=88=c [63 ]a score=-4.43671, c=-0.085263, perm=8, hash=f093be058e3f2e
40 null_char score=-4.60841, c=-0.0850318, perm=0, hash=f093be058e3f2e prev:null_char score=-4.52338, c=-0.0866698, perm=0, hash=f093be058e3f2e
41 label=84, uid=86=l [6c ]a score=-4.69345, c=-0.0850427, End perm=8, hash=1bce4a2a8dce8899 prev:null_char score=-4.60841, c=-0.0850318, perm=0, hash=f093be058e3f2e
42 label=84, uid=86=l [6c ]a score=-4.80252, c=-0.109067, perm=8, hash=1bce4a2a8dce8899 prev:label=84, uid=86=l [6c ]a score=-4.69345, c=-0.0850427, End perm=8, hash=1bce4a2a8dce8899
43 label=84, uid=86=l [6c ]a score=-4.89457, c=-0.0920493, perm=8, hash=1bce4a2a8dce8899 prev:label=84, uid=86=l [6c ]a score=-4.80252, c=-0.109067, perm=8, hash=1bce4a2a8dce8899
44 label=84, uid=86=l [6c ]a score=-4.97963, c=-0.0850596, perm=8, hash=1bce4a2a8dce8899 prev:label=84, uid=86=l [6c ]a score=-4.89457, c=-0.0920493, perm=8, hash=1bce4a2a8dce8899
45 null_char score=-5.06463, c=-0.085006, perm=0, hash=1bce4a2a8dce8899 prev:label=84, uid=86=l [6c ]a score=-4.97963, c=-0.0850596, perm=8, hash=1bce4a2a8dce8899
46 null_char score=-5.14988, c=-0.0852435, perm=0, hash=1bce4a2a8dce8899 prev:null_char score=-5.06463, c=-0.085006, perm=0, hash=1bce4a2a8dce8899
47 label=73, uid=75=e [65 ]a score=-5.23488, c=-0.0850003, End perm=8, hash=cd5b7e3349a524e7 prev:null_char score=-5.14988, c=-0.0852435, perm=0, hash=1bce4a2a8dce8899
48 label=73, uid=75=e [65 ]a score=-5.32002, c=-0.0851438, perm=8, hash=cd5b7e3349a524e7 prev:label=73, uid=75=e [65 ]a score=-5.23488, c=-0.0850003, End perm=8, hash=cd5b7e3349a524e7
49 label=73, uid=75=e [65 ]a score=-5.4057, c=-0.0856761, perm=8, hash=cd5b7e3349a524e7 prev:label=73, uid=75=e [65 ]a score=-5.32002, c=-0.0851438, perm=8, hash=cd5b7e3349a524e7
50 label=73, uid=75=e [65 ]a score=-5.49112, c=-0.0854247, perm=8, hash=cd5b7e3349a524e7 prev:label=73, uid=75=e [65 ]a score=-5.4057, c=-0.0856761, perm=8, hash=cd5b7e3349a524e7
51 null_char score=-5.57612, c=-0.0850005, perm=0, hash=cd5b7e3349a524e7 prev:label=73, uid=75=e [65 ]a score=-5.49112, c=-0.0854247, perm=8, hash=cd5b7e3349a524e7
52 null_char score=-5.66181, c=-0.0856828, perm=0, hash=cd5b7e3349a524e7 prev:null_char score=-5.57612, c=-0.0850005, perm=0, hash=cd5b7e3349a524e7
53 label=82, uid=84=a [61 ]a score=-5.74681, c=-0.0850002, End perm=8, hash=41eee080e0d189c6 prev:null_char score=-5.66181, c=-0.0856828, perm=0, hash=cd5b7e3349a524e7
54 label=82, uid=84=a [61 ]a score=-5.83184, c=-0.0850337, perm=8, hash=41eee080e0d189c6 prev:label=82, uid=84=a [61 ]a score=-5.74681, c=-0.0850002, End perm=8, hash=41eee080e0d189c6
55 label=82, uid=84=a [61 ]a score=-5.91808, c=-0.0862392, perm=8, hash=41eee080e0d189c6 prev:label=82, uid=84=a [61 ]a score=-5.83184, c=-0.0850337, perm=8, hash=41eee080e0d189c6
56 label=82, uid=84=a [61 ]a score=-6.00311, c=-0.0850275, perm=8, hash=41eee080e0d189c6 prev:label=82, uid=84=a [61 ]a score=-5.91808, c=-0.0862392, perm=8, hash=41eee080e0d189c6
57 null_char score=-6.08811, c=-0.0850001, perm=0, hash=41eee080e0d189c6 prev:label=82, uid=84=a [61 ]a score=-6.00311, c=-0.0850275, perm=8, hash=41eee080e0d189c6
58 null_char score=-6.18576, c=-0.0976547, perm=0, hash=41eee080e0d189c6 prev:null_char score=-6.08811, c=-0.0850001, perm=0, hash=41eee080e0d189c6
59 label=68, uid=70=r [72 ]a score=-6.27077, c=-0.0850028, End perm=8, hash=c7ccd80937308825 prev:null_char score=-6.18576, c=-0.0976547, perm=0, hash=41eee080e0d189c6
60 label=68, uid=70=r [72 ]a score=-6.35581, c=-0.0850432, perm=8, hash=c7ccd80937308825 prev:label=68, uid=70=r [72 ]a score=-6.27077, c=-0.0850028, End perm=8, hash=c7ccd80937308825
61 label=68, uid=70=r [72 ]a score=-6.4409, c=-0.0850943, perm=8, hash=c7ccd80937308825 prev:label=68, uid=70=r [72 ]a score=-6.35581, c=-0.0850432, perm=8, hash=c7ccd80937308825
62 null_char score=-6.52711, c=-0.0862095, perm=0, hash=c7ccd80937308825 prev:label=68, uid=70=r [72 ]a score=-6.4409, c=-0.0850943, perm=8, hash=c7ccd80937308825
63 null_char score=-6.61215, c=-0.0850359, perm=0, hash=c7ccd80937308825 prev:null_char score=-6.52711, c=-0.0862095, perm=0, hash=c7ccd80937308825
64 null_char score=-6.69715, c=-0.0850039, perm=0, hash=c7ccd80937308825 prev:null_char score=-6.61215, c=-0.0850359, perm=0, hash=c7ccd80937308825

Second choice path:
2 91=w [77 ]a r=0.608491, c=-0.0934861, s=1, e=1, perm=8
8 80=i [69 ]a r=0.426893, c=-0.0868062, s=0, e=1, perm=8
14 76=s [73 ]a r=0.750678, c=-0.153684, s=0, e=1, perm=8
20 0=  [20 ] r=0.468131, c=-0.191574, s=0, e=0, perm=8
25 16=- [2d ]p r=1.40478, c=-0.244496, s=0, e=0, perm=2
30 0=  [20 ] r=0.773222, c=-0.198616, s=0, e=0, perm=2
36 88=c [63 ]a r=0.976695, c=-0.207406, s=0, e=0, perm=2
41 86=l [6c ]a r=1.22157, c=-0.245401, s=0, e=0, perm=2
47 75=e [65 ]a r=1.15086, c=-0.192771, s=0, e=0, perm=2
53 84=a [61 ]a r=1.15196, c=-0.194038, s=0, e=0, perm=2
59 70=r [72 ]a r=1.5616, c=-0.219723, s=0, e=0, perm=2
Path total rating = 10.4949
2 91=w [77 ]a r=0.608491, c=-0.0934861, s=1, e=1, perm=8
8 80=i [69 ]a r=0.426893, c=-0.0868062, s=0, e=1, perm=8
14 76=s [73 ]a r=0.750678, c=-0.153684, s=0, e=1, perm=8
20 0=  [20 ] r=0.468131, c=-0.191574, s=0, e=0, perm=8
25 16=- [2d ]p r=1.01603, c=-0.244496, s=0, e=0, perm=2
30 0=  [20 ] r=0.732398, c=-0.085002, s=0, e=0, perm=0
36 88=c [63 ]a r=0.434087, c=-0.0921804, s=1, e=1, perm=8
41 86=l [6c ]a r=0.542921, c=-0.109067, s=0, e=1, perm=8
47 75=e [65 ]a r=0.511494, c=-0.0856761, s=0, e=1, perm=8
53 84=a [61 ]a r=0.511984, c=-0.0862392, s=0, e=1, perm=8
59 70=r [72 ]a r=0.694044, c=-0.0976547, s=0, e=1, perm=8
Path total rating = 6.69715
Best choice: accepted=1, adaptable=0, done=1 : Lang result : wis : R=1.78606, C=-1.34102, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM    NORM    NORM
str     w       i       s
state:  1       1       1
C       -0.093  -0.087  -0.154
Best choice: accepted=1, adaptable=0, done=1 : Lang result : - : R=1.01603, C=-1.71147, F=1, Perm=2, xht=[0,3.40282e+38], ambig=0
pos     NORM
str     -
state:  1
C       -0.244
Best choice: accepted=1, adaptable=0, done=1 : Lang result : clear : R=2.69453, C=-0.763471, F=1, Perm=8, xht=[0,3.40282e+38], ambig=0
pos     NORM    NORM    NORM    NORM    NORM
str     c       l       e       a       r
state:  1       1       1       1       1
C       -0.092  -0.109  -0.086  -0.086  -0.098
1 new words better than 0 old words: r: 1.78606 v 0 c: -1.34102 v 0 valid dict: 1 v 0
1 new words better than 0 old words: r: 1.01603 v 0 c: -1.71147 v 0 valid dict: 0 v 0
1 new words better than 0 old words: r: 2.69453 v 0 c: -0.763471 v 0 valid dict: 1 v 0
Pass1: clear [c [63 ]a l [6c ]a e [65 ]a a [61 ]a r [72 ]a ]

Expected Behavior:

Clearly readable text should be recognized without failure.

Suggested Fix:

[stweil]

I tried git bisect now with tessdata_fast/eng and did not find a Tesseract release without that issue. Even 4.0.0 creates the double content in my test.

[amitdo]

AFAIK, fast was trained on inverted text and non-inverted text and on upright pages and upside down pages.

[rmast]

I'm not convinced it's inversion related. I think it already comes from somewhere where segments are propagated into each other, probably searching underlines. If I run this statement wis-clear is still double, and print is still missing:

tesseract --dpi 300 -l Latin 175789293-f39ddfdb-6f3e-4598-8d16-80a1f4a88b36.jpg outputwithoutinvert

So textline inversion might be removed as a label.

[rmast]

By the way, this one is compiled without legacy, so it's in the new parts

[rmast]

Testing underline on blob at (2149,3149)->(2396,3189), base=3160
Occs:247 247 247
Testing underline on blob at (2149,3103)->(2396,3144), base=3085
Occs:0 0 247
Underlined blob at:Bounding box=(2149,3103)->(2396,3144)
Was:Bounding box=(2149,3103)->(2396,3144)
Segmenting baseline of 19 blobs at (2149,3149)
Made 1 segments on row at (2355,3149)
Segmenting baseline of 11 blobs at (2164,3113)
Made 1 segments on row at (2307,3114)

Input height=26.25, Estimate x-height=40 pixels, jumplimit=6.00
1(2168,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
2(2189,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
3(2209,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
4(2230,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
5(2251,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
6(2271,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
7(2292,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
8(2313,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
9(2333,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
10(2354,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
11(2375,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
1(2168,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0
0(2149,3149), Diff=-11.00, Delta=0.000, Drift=0.000, P=0

Input height=26.25, Estimate x-height=16 pixels, jumplimit=2.40
4(2269,3114), Diff=-0.32, Delta=0.000, Drift=0.000, P=0
5(2286,3114), Diff=-0.47, Delta=-0.148, Drift=0.000, P=0
6(2293,3114), Diff=-0.62, Delta=-0.247, Drift=-0.049, P=0
7(2310,3114), Diff=-0.81, Delta=-0.362, Drift=-0.132, P=0
8(2327,3115), Diff=0.00, Delta=0.573, Drift=-0.252, P=0
4(2269,3114), Diff=-0.32, Delta=0.000, Drift=0.000, P=0
3(2224,3114), Diff=0.25, Delta=0.568, Drift=0.000, P=0
2(2217,3115), Diff=1.38, Delta=1.514, Drift=0.189, P=0
1(2194,3115), Diff=1.57, Delta=1.195, Drift=0.694, P=0
0(2164,3113), Diff=0.00, Delta=-0.771, Drift=1.092, P=0
First turn is 0 at (2169,3113)
Turn 1 is 1 at (2204,3115), mid pt is 0@2169, final @2187
Segmenting baseline of 34 blobs at (1902,2842)
Made 1 segments on row at (2347,2841)

[stweil]

So textline inversion might be removed as a label.

I also no longer think that it is related to textline inversion as the issue also occurs in old versions like 4.0.0. My previous git bisect result was misleading.

By the way, this one is compiled without legacy, so it's in the new parts

The layout detection is mostly still old code.

[rmast]

I've now pinpointed the disappearing upper boundingbox from
Block1 textord.cppBlock 28Bounding box=(2149,3103)->(2396,3189)
Bounding box=(2149,3149)->(2396,3189)
Bounding box=(2149,3103)->(2396,3144)
Bounding box=(2194,3114)->(2237,3137)
Bounding box=(2249,3121)->(2257,3125)
Bounding box=(2269,3114)->(2336,3137)
/Block
as disappearing in textord.cpp
// Remove empties.
cleanup_blocks(PSM_WORD_FIND_ENABLED(pageseg_mode), blocks);

[rmast]

This might be involved:

B:28 R:1 -- Can't do isolated row stats.
B:28 R:1 -- Inadequate certain spaces.

tesseract -c textord_restore_underlines=1 --dpi 300 -l Latin -c textord_noise_rejrows=0 -c textord_debug_block=28 -c textord_noise_debug=1 -c textord_debug_tabfind=1 -c textord_debug_bugs=1 -c textord_show_final_rows=1 -c tosp_debug_level=6 /home/rmast/175789293-f39ddfdb-6f3e-4598-8d16-80a1f4a88b36.jpg outputdebug85online

[amitdo]

With this image, I get an empty output with all available eng/Latin models.

[amitdo]

After upscaling (2x)

output:

> wis - clear

[amitdo]

After upscaling (4x)

output:

> print

> Wis - clear