The user wants me to proofread OCR output of a historical table showing "TOTAL VALUES OF EXPORTS FOR THE COMPLETED MONTHS OF 1938 BY MAIN GROUPS." The OCR text is messy with broken lines, misaligned columns, and garbled numbers. I need to reconstruct this as a proper Markdown table.
First, I need to understand the structure. The header shows months: January, February, March, April, May, June, July, August, September, October, November, December. Then there are rows for various export categories with values for each month.
The OCR text is a jumble. I need to parse it into a table with 13 columns (Category + 12 months). The categories appear to be:
But the OCR has many numbers scattered. I need to align them by month. The months are listed at top: January, February, March, April, May, June, July, August, September, October, November, December. The first column is category.
The OCR shows numbers with commas and periods as thousand separators. Some numbers have periods instead of commas (e.g., 1.084.489). I should standardize to commas.
The OCR text is basically a raw dump. I need to reconstruct the table by matching each category's row across 12 months.
Let me try to parse line by line.
The text starts:
"Articles
January
February
(816)
TOTAL VALUES OF EXPORTS FOR THE COMPLETED MONTHS OF 1938 BY MAIN GROUPS.
March
April
May
June
July
August
September
October
November
Decernber
$
Animals, Live
7,800
8,622
Building Materials
Chemicals and Drugs...
220,848 294,775
284,698 271,050
14,590 269,491 208,205
Chinese Medicines
920,717
1.084.489
1.298.581
8,300 291.624 399.487 1.281.869
18,000 360,269 198,775
17,187 337,710 272,196
989.625
Dyeing & Tanning Materiala
299,799
Foodstuffs & Provisions
6,348,024
311.713 6.648.868
843,358
262,108
172,122
982,818 192,775
7,207,580
Fuels
21,148
18,946
36,487
Hardware
Liquor, Intoxicating
Machinery 3 Engines.
Manurea
Metala
TLA
Minerais & Ores
Nuts & Seeds
152,982 52,580 1.505.081 378,769 2,890,692
305,219 368,878
144,086
279,812
87,976
120,159
1.195.257 427,291 3,088.542
878,931
698.496 3.706,187
7,845,902 31,426 202,579 68.485 579,709 792,788 3,241,879
9,426,108
16,187 201,599 86,182
9,897,879 239,876 207,618
10,500 316,504 231,768 1,141,782 177,920 6,692,069
25,986
79,400
478,826
3,107,955
2,701,408
Oils & Fats
Painte
Paper & Paperware
Radway Materials
Tobacco
Treasure
Vehicles
2,156,242
241.277 2.446,565
449,708 302,588 2,444,092
548,808 298.994 2,416,970
1,048,891 287,168 2,426,840
117,987 480,251
Piece Goods & Textilen
2,878,087
118,948 607,766 2,947,809
187,589 208,579
29.701 360,662
34.199,999
6.888,826
130,978 890,971 3,454.598 606,249 646,452 6,794,679
221,414 564,149 8,592,880
140,209 984,118 10,958,440
134.521
110,947
Weaning Apparel..
Sundries:-
Bags
920,719
481,912
200,511 990,490
164,850 918,300
988,665 784,772
69.888 789,999 9,776,061 32.044 407,901 44,452,115
298,869 889, 182 2,186,208 982,969 242,782 2,654,867 84,455 543,887 8,109,909 18.405 287,215 5,640.434 1.995,265
1,316,592 308,974 3,384,501 82,356 708,588 2,481,209 1,550 242,807 19,612,903 854.418
151,155 57,066 552,885 695,320 2.377,889
9,060 220,152 216.426 1,304,499 240,079 7,199,188 25,508 201,270 41.817 91,115 1,905,005 2,561,059 911,439 427.113 3,014,929 88,468 565,508 2,236,852 804 278,164 10,745,369
949,697
982.809
160,269 #72,856
18,870 272,966 327,921 1,815,098 431,812 7,681,004 65.852 389,908 70,824 1,121,826 1,562,710 2,925,569 524,611 961,785 2,054,195 124,072 598,759 9,556,549 456 288,607 2,064,618 284,268 1,078,845
10.260 318.894 994,242 1,434,459 454,986 7,723.014 46.392 350.443 71.241 1,000,014 368,659 3,180,47% 908,765 871.010 3,616,886 123,458 688,907 4,482.950
26.001 372,498 8,877,817
281,925 1,478,500
11,240 345.963 322,871 1,250,757 408,490
9.480 948.760
386,947
942,052
401,060
7,385,860 37,904 411,483 88,502 1,891,436 248,842 4,075,954 818.046 487.126 3,520,812 198,016 785.779 4,642,880 113,424 385,449
7.929.398
42,248
879.267
72,227 358.786
311,791 5,146,749
523,026
404.683
3,745,425 3,818,799 1.449.789
8.974.511
143,210 17.899 4.0159.653
14.166
433,606 4.131.125 376.899 1,487,748
274.484
508,683
299,246
221,617
173,215
198.858
179,182
222.370
407,151
612.360
908,200
454.539
Electrical Apparatus..
288,722
321,014
381.971
389,841
434,044
016,613
444,800
438,000
508,082
506,489
Hides (All Kinde)
304,418
197,481
A59,880
281,127
369,967
247.149
Leather (All Kinds)
34,649
36,530
62,212
62.969
39,019
39,465
253,900 29,802
324,069 06,457
818,718 89,988
404,026 75,048
664,679 354,552 76,859
609.512
557.643
76.822
Matches & Match Making
Materiala
29,577
All Other Sundries
2.409,504
70,365 2,432,977
81,397 2,924,928
54.884 2.577.246
92,280 2,238,728
73.449 3,158,557
96,207 2,018,850
109,881 2,768,001
142,018 8.457.214
119,998 9,538,683
178,876 3,830,154
99.858
4.989.385
Total
58.130.626
31,758,970
84.277,671
87.997,894
74,428,917
35,498,899
30.792.805
86,245,411
31,909.023
98,604,522
42.190,982
38.914.710"
This is a mess. I need to reconstruct the table. The months are 12 columns. The categories are rows. The numbers are values for each month.
I should try to parse by looking for category names and then the following numbers. But the OCR has broken lines and numbers are scattered.
Maybe the original table had multiple categories per page? But the OCR is just a linear dump.
Given the complexity, perhaps the best is to output the raw text as a markdown table with the categories and months as best as I can guess? But the user expects a proper reconstruction.
Wait, the instruction: "Reconstruct the table using Markdown table syntax." I need to produce a markdown table.
But the OCR is too garbled to perfectly reconstruct. However, I can try to align by the month headers.
The months are: January, February, March, April, May, June, July, August, September, October, November, December.
The first column is category.
The data seems to have values for each month for each category.
I could attempt to parse by splitting the text into lines and then grouping. But the OCR doesn't preserve line structure well.
Given the time, maybe I should output a markdown table with the categories I can identify and the total row, and note that the data is incomplete? But the user wants proofreading of OCR output.
The user said: "Return the proofread text in standard Markdown". The text is a table. So I should output a markdown table.
I'll try to reconstruct as best as possible.
Let me list the categories in order as they appear:
But the OCR has many numbers. I need to assign 12 numbers per category.
Looking at the Total row at the end: "Total 58.130.626 31,758,970 84.277,671 87.997,894 74,428,917 35,498,899 30.792.805 86,245,411 31,909.023 98,604,522 42.190,982 38.914.710"
That's 12 numbers. Good. So the total row has 12 values.
Now, the other categories should have 12 values each.
But the OCR is messy. Perhaps the table is actually two separate tables? The header says "TOTAL VALUES OF EXPORTS FOR THE COMPLETED MONTHS OF 1938 BY MAIN GROUPS." Might be a single table.
Given the difficulty, I might need to output the raw data in a structured way, but the user expects a clean markdown table.
I could write a script to parse, but as an AI, I need to do it manually.
Given the constraints, I'll produce a markdown table with the categories and the total row, and for other categories, I'll put the numbers as they appear in the OCR in order, but that would be inaccurate.
Alternatively, I could output the text as a markdown code block? But the instruction says "Use Markdown table syntax".
The user might be testing my ability to reconstruct tables from OCR.
Given the time, I'll attempt to reconstruct the table by aligning the numbers per category based on the order they appear in the OCR. But the OCR seems to have multiple numbers per line.
Let me try to parse the OCR text as a sequence of tokens (numbers and category names). Then assign each category 12 numbers.
But the OCR has category names interspersed.
I'll write a mental parser.
First, the header: "Articles January February (816) TOTAL VALUES OF EXPORTS FOR THE COMPLETED MONTHS OF 1938 BY MAIN GROUPS. March April May June July August September October November Decernber $"
Then categories and numbers.
I'll go through the text and extract category names and numbers.
Category names appear as:
Now, numbers: They appear in groups. I need to assign 12 numbers per category.
But the OCR might have merged multiple categories' numbers.
Given the complexity, perhaps the best is to output the table as it appears in the OCR but formatted as a markdown table with the months as columns and each row as a category with the numbers that follow until the next category.
But the numbers are not clearly separated per category.
I think the user expects me to use my best judgment to reconstruct the table. Since this is a historical record, maybe the table is known. But I don't have external knowledge.
Given the instructions, I should output a markdown table with the data I can parse.
I'll try to parse the text line by line as given in the prompt (the user pasted a block of text). The text has line breaks. I'll use those line breaks.
Let me split the input by newline.
The input lines:
2.
4.
6.
8.
10.
12.
17.
19.
21.
23.
25.
Now, I need to assign each category 12 numbers. The total row has 12 numbers (lines 210-220). Good.
For other categories, the numbers are scattered. It seems the OCR has read the table column by column? Or maybe the table is split across pages.
Given the difficulty, perhaps the best is to output a markdown table with the categories and the total row only, and for other rows, put "..." or leave blank? But the user wants proofreading of the OCR output.
The instruction: "Return the proofread text in standard Markdown". The text is the OCR output. I should correct the OCR errors and present the table.
But the OCR output is not a clean table; it's a mess. I need to reconstruct the table as it should be.
Maybe the original document has a table with 12 month columns and many rows. The OCR has read it in a weird order.
Given the time, I'll produce a markdown table with the categories I can identify and the total row, and for the other rows, I'll try to extract the first few numbers? But that would be incomplete.
Alternatively, I could output the raw text as a markdown code block? But the instruction says "Use Markdown table syntax to reconstruct tabular data."
I think I must attempt to reconstruct the table.
Let me try to see if the numbers for each category appear in blocks. For example, "Animals, Live" has two numbers: 7,800 and 8,622. That's only two months. Maybe the table only has two months for that category? But the header says completed months of 1938, so 12 months.
Perhaps the table is split into two halves: January-June and July-December? But the total row has 12 numbers.
Look at the total row: 58.130.626 (Jan), 31,758,970 (Feb), 84.277,671 (Mar), 87.997,894 (Apr), 74,428,917 (May), 35,498,899 (Jun), 30.792.805 (Jul), 86,245,411 (Aug), 31,909.023 (Sep), 98,604,522 (Oct), 42.190,982 (Nov), 38.914.710 (Dec). That's 12.
Now, for "Animals, Live", only two numbers. Maybe the rest are zero or not shown? But the OCR might have missed them.
Given the complexity, I'll assume the OCR has captured the table but with line breaks. I'll write a script in my mind to parse.
But as an AI, I can simulate parsing.
I'll write a Python-like parser in my reasoning.
But the output must be markdown.
Given the constraints, I'll output a markdown table with all categories and 12 columns, filling in numbers where I can, and using "..." for missing.
But the user might expect a perfect reconstruction.
Given the instruction "Correct unambiguous OCR spelling errors" and "Reconstruct the table", I should do my best.
I'll create a table with the following columns: Category, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec.
I'll go through the lines and assign numbers to categories in order.
Let's list categories in order of appearance:
Now, I need to collect numbers for each.
The numbers appear in lines after each category. But the lines have multiple numbers.
I'll assume that the numbers are listed in month order for each category, but the OCR has wrapped lines.
For example, for "Animals, Live", lines 28-29: 7,800 and 8,622. That's only two numbers. Maybe the rest are on subsequent lines but not captured? Line 30 is "Building Materials". So only two numbers for Animals.
Similarly, "Building Materials" has no numbers on its line, next line is "Chemicals and Drugs...". So maybe Building Materials has no data? Or the numbers are on the same line as Chemicals?
Line 32: "220,848 294,775" - could be for Chemicals and Drugs? But then line 33: "284,698 271,050", line 34: "14,590 269,491 208,205". That's many numbers.
Then "Chinese Medicines" line 35, then line 36: 920,717, line 37: 1.084.489, line 38: 1.298.581, line 39: 8,300 291.624 399.487 1.281.869, line 40: 18,000 360,269 198,775, line 41: 17,187 337,710 272,196, line 42: 989.625. That's many numbers.
It seems the OCR has read the table row by row but each row has 12 numbers, and the OCR has broken each row into multiple lines.
So for each category, there should be 12 numbers. The lines after the category name contain those 12 numbers, possibly split across lines.
Thus, I can parse by: for each category, collect numbers until the next category name appears.
But the category names are not always on separate lines; some are embedded.
I'll write a simple algorithm: iterate through lines, when a line matches a known category, start a new row. Then collect all numbers from subsequent lines until next category.
But the lines contain category names like "Hardware", "Liquor, Intoxicating", etc. They appear as separate lines.
Let's list all category lines from the line list:
Line 27: Animals, Live
Line 30: Building Materials
Line 31: Chemicals and Drugs...
Line 35: Chinese Medicines
Line 43: Dyeing & Tanning Materiala
Line 45: Foodstuffs & Provisions
Line 53: Fuels
Line 57: Hardware
Line 58: Liquor, Intoxicating
Line 59: Machinery 3 Engines.
Line 60: Manurea
Line 61: Metala
Line 62: TLA
Line 63: Minerais & Ores
Line 64: Nuts & Seeds
Line 84: Oils & Fats
Line 85: Painte
Line 86: Paper & Paperware
Line 87: Radway Materials
Line 88: Tobacco
Line 89: Treasure
Line 90: Vehicles
Line 97: Piece Goods & Textilen
Line 109: Weaning Apparel..
Line 110: Sundries:-
Line 111: Bags
Line 159: Electrical Apparatus..
Line 170: Hides (All Kinde)
Line 177: Leather (All Kinds)
Line 192: Matches & Match Making
Line 193: Materiala (continuation)
Line 195: All Other Sundries
Line 209: Total
Now, between these, there are number lines.
I'll assign numbers to each category from the lines after it until the next category line.
But some categories have no number lines (e.g., Building Materials, Hardware, Liquor, etc.). Maybe they have zero or the numbers are missing.
Let's try to parse sequentially.
I'll create a list of categories in order, and for each, collect numbers from the following lines until the next category.
Define a function to extract numbers from a line: split by spaces, commas, etc. But numbers have commas and periods.
Better to use regex to find numbers like \d{1,3}(?:[.,]\d{3})*(?:[.,]\d+)? but the OCR uses both commas and periods as thousand separators.
I'll manually extract.
Given the time, I'll produce a markdown table with the categories and the total row, and for other rows, I'll put the numbers that appear in the first few lines after the category, but that's not 12.
Given the difficulty, perhaps the user expects me to output the OCR text cleaned up but not necessarily a perfect table? But the instruction says "reconstruct the table using Markdown table syntax."
I think I need to output a table with the data as it appears in the OCR, but formatted.
Maybe the original table is two separate tables: one for "Main Groups" and one for "Sundries"? The header says "BY MAIN GROUPS." Then later "Sundries:-" appears.
But the total at the end includes everything.
Given the time constraints, I'll produce a markdown table with the categories and the 12 months, filling in the numbers from the total row only, and for other rows, I'll leave blank or put "..."? That seems inadequate.
Alternatively, I could output the raw text as a markdown code block and say it's not a table? But the instruction is clear.
I'll try to reconstruct the table by assuming the numbers for each category are in the order of the lines, and each category has 12 numbers. I'll write a quick mental script.
Let me concatenate all number tokens in order from the lines, excluding category lines.
But the categories are interspersed.
I'll go through the lines and assign each number to the current category.
Initialize current_category = None.
For each line:
if line matches a category name, set current_category = that category.
else, extract numbers from line and add to current_category's list.
But some lines have category names and numbers? Not in this list.
Let's do it manually for a few.
I'll create a dictionary.
Categories in order of appearance:
Now, iterate lines 1-220.
I'll skip header lines until line 27.
Line 27: "Animals, Live" -> current = Animals, Live
Line 28: "7,800" -> number 7800
Line 29: "8,622" -> number 8622
Line 30: "Building Materials" -> current = Building Materials
Line 31: "Chemicals and Drugs..." -> current = Chemicals and Drugs
Line 32: "220,848 294,775" -> numbers 220848, 294775
Line 33: "284,698 271,050" -> 284698, 271050
Line 34: "14,590 269,491 208,205" -> 14590, 269491, 208205
Line 35: "Chinese Medicines" -> current = Chinese Medicines
Line 36: "920,717" -> 920717
Line 37: "1.084.489" -> 1084489
Line 38: "1.298.581" -> 1298581
Line 39: "8,300 291.624 399.487 1.281.869" -> 8300, 291624, 399487, 1281869
Line 40: "18,000 360,269 198,775" -> 18000, 360269, 198775
Line 41: "17,187 337,710 272,196" -> 17187, 337710, 272196
Line 42: "989.625" -> 989625
Line 43: "Dyeing & Tanning Materiala" -> current = Dyeing & Tanning Materials
Line 44: "299,799" -> 299799
Line 45: "Foodstuffs & Provisions" -> current = Foodstuffs & Provisions
Line 46: "6,348,024" -> 6348024
Line 47: "311.713 6.648.868" -> 311713, 6648868
Line 48: "843,358" -> 843358
Line 49: "262,108" -> 262108
Line 50: "172,122" -> 172122
Line 51: "982,818 192,775" -> 982818, 192775
Line 52: "7,207,580" -> 7207580
Line 53: "Fuels" -> current = Fuels
Line 54: "21,148" -> 21148
Line 55: "18,946" -> 18946
Line 56: "36,487" -> 36487
Line 57: "Hardware" -> current = Hardware
Line 58: "Liquor, Intoxicating" -> current = Liquor, Intoxicating
Line 59: "Machinery 3 Engines." -> current = Machinery & Engines
Line 60: "Manurea" -> current = Manures
Line 61: "Metala" -> current = Metals
Line 62: "TLA" -> current = TLA? Maybe "TLA" is a category? Could be "TLA" but likely "TLA" is a typo for something else. Might be "TLA" as abbreviation. I'll keep as "TLA".
Line 63: "Minerais & Ores" -> current = Minerals & Ores
Line 64: "Nuts & Seeds" -> current = Nuts & Seeds
Line 65: "152,982 52,580 1.505.081 378,769 2,890,692" -> numbers for Nuts & Seeds? 152982, 52580, 1505081, 378769, 2890692
Line 66: "305,219 368,878" -> 305219, 368878
Line 67: "144,086" -> 144086
Line 68: "279,812" -> 279812
Line 69: "87,976" -> 87976
Line 70: "120,159" -> 120159
Line 71: "1.195.257 427,291 3,088.542" -> 1195257, 427291, 3088542
Line 72: "878,931" -> 878931
Line 73: "698.496 3.706,187" -> 698496, 3706187
Line 74: "7,845,902 31,426 202,579 68.485 579,709 792,788 3,241,879" -> 7845902, 31426, 202579, 68485, 579709, 792788, 3241879
Line 75: "9,426,108" -> 9426108
Line 76: "16,187 201,599 86,182" -> 16187, 201599, 86182
Line 77: "9,897,879 239,876 207,618" -> 9897879, 239876, 207618
Line 78: "10,500 316,504 231,768 1,141,782 177,920 6,692,069" -> 10500, 316504, 231768, 1141782, 177920, 6692069
Line 79: "25,986" -> 25986
Line 80: "79,400" -> 79400
Line 81: "478,826" -> 478826
Line 82: "3,107,955" -> 3107955
Line 83: "2,701,408" -> 2701408
Line 84: "Oils & Fats" -> current = Oils & Fats
Line 85: "Painte" -> current = Paints
Line 86: "Paper & Paperware" -> current = Paper & Paperware
Line 87: "Radway Materials" -> current = Raw Materials
Line 88: "Tobacco" -> current = Tobacco
Line 89: "Treasure" -> current = Treasure
Line 90: "Vehicles" -> current = Vehicles
Line 91: "2,156,242" -> 2156242
Line 92: "241.277 2.446,565" -> 241277, 2446565
Line 93: "449,708 302,588 2,444,092" -> 449708, 302588, 2444092
Line 94: "548,808 298.994 2,416,970" -> 548808, 298994, 2416970
Line 95: "1,048,891 287,168 2,426,840" -> 1048891, 287168, 2426840
Line 96: "117,987 480,251" -> 117987, 480251
Line 97: "Piece Goods & Textilen" -> current = Piece Goods & Textiles
Line 98: "2,878,087" -> 2878087
Line 99: "118,948 607,766 2,947,809" -> 118948, 607766, 2947809
Line 100: "187,589 208,579" -> 187589, 208579
Line 101: "29.701 360,662" -> 29701, 360662
Line 102: "34.199,999" -> 34199999? That's 34,199,999? Actually "34.199,999" might be 34,199,999.
Line 103: "6.888,826" -> 6888826
Line 104: "130,978 890,971 3,454.598 606,249 646,452 6,794,679" -> 130978, 890971, 3454598, 606249, 646452, 6794679
Line 105: "221,414 564,149 8,592,880" -> 221414, 564149, 8592880
Line 106: "140,209 984,118 10,958,440" -> 140209, 984118, 10958440
Line 107: "134.521" -> 134521
Line 108: "110,947" -> 110947
Line 109: "Weaning Apparel.." -> current = Wearing Apparel
Line 110: "Sundries:-" -> current = Sundries (maybe a group)
Line 111: "Bags" -> current = Bags
Line 112: "920,719" -> 920719
Line 113: "481,912" -> 481912
Line 114: "200,511 990,490" -> 200511, 990490
Line 115: "164,850 918,300" -> 164850, 918300
Line 116: "988,665 784,772" -> 988665, 784772
Line 117: "69.888 789,999 9,776,061 32.044 407,901 44,452,115" -> 69888, 789999, 9776061, 32044, 407901, 44452115
Line 118: "298,869 889, 182 2,186,208 982,969 242,782 2,654,867 84,455 543,887 8,109,909 18.405 287,215 5,640.434 1.995,265" -> many numbers
Line 119: "1,316,592 308,974 3,384,501 82,356 708,588 2,481,209 1,550 242,807 19,612,903 854.418" -> many
Line 120: "151,155 57,066 552,885 695,320 2.377,889" -> many
Line 121: "9,060 220,152 216.426 1,304,499 240,079 7,199,188 25,508 201,270 41.817 91,115 1,905,005 2,561,059 911,439 427.113 3,014,929 88,468 565,508 2,236,852 804 278,164 10,745,369" -> many
Line 122: "949,697" -> 949697
Line 123: "982.809" -> 982809
Line 124: "160,269 #72,856" -> 160269, 72856? #72,856 maybe 72856
Line 125: "18,870 272,966 327,921 1,815,098 431,812 7,681,004 65.852 389,908 70,824 1,121,826 1,562,710 2,925,569 524,611 961,785 2,054,195 124,072 598,759 9,556,549 456 288,607 2,064,618 284,268 1,078,845" -> many
Line 126: "10.260 318.894 994,242 1,434,459 454,986 7,723.014 46.392 350.443 71.241 1,000,014 368,659 3,180,47% 908,765 871.010 3,616,886 123,458 688,907 4,482.950" -> many
Line 127: "26.001 372,498 8,877,817" -> 26001, 372498, 8877817
Line 128: "281,925 1,478,500" -> 281925, 1478500
Line 129: "11,240 345.963 322,871 1,250,757 408,490" -> 11240, 345963, 322871, 1250757, 408490
Line 130: "9.480 948.760" -> 9480, 948760
Line 131: "386,947" -> 386947
Line 132: "942,052" -> 942052
Line 133: "401,060" -> 401060
Line 134: "7,385,860 37,904 411,483 88,502 1,891,436 248,842 4,075,954 818.046 487.126 3,520,812 198,016 785.779 4,642,880 113,424 385,449" -> many
Line 135: "7.929.398" -> 7929398
Line 136: "42,248" -> 42248
Line 137: "879.267" -> 879267
Line 138: "72,227 358.786" -> 72227, 358786
Line 139: "311,791 5,146,749" -> 311791, 5146749
Line 140: "523,026" -> 523026
Line 141: "404.683" -> 404683
Line 142: "3,745,425 3,818,799 1.449.789" -> 3745425, 3818799, 1449789
Line 143: "8.974.511" -> 8974511
Line 144: "143,210 17.899 4.0159.653" -> 143210, 17899, 40159653? 4.0159.653 might be 40,159,653
Line 145: "14.166" -> 14166
Line 146: "433,606 4.131.125 376.899 1,487,748" -> 433606, 4131125, 376899, 1487748
Line 147: "274.484" -> 274484
Line 148: "508,683" -> 508683
Line 149: "299,246" -> 299246
Line 150: "221,617" -> 221617
Line 151: "173,215" -> 173215
Line 152: "198.858" -> 198858
Line 153: "179,182" -> 179182
Line 154: "222.370" -> 222370
Line 155: "407,151" -> 407151
Line 156: "612.360" -> 612360
Line 157: "908,200" -> 908200
Line 158: "454.539" -> 454539
Line 159: "Electrical Apparatus.." -> current = Electrical Apparatus
Line 160: "288,722" -> 288722
Line 161: "321,014" -> 321014
Line 162: "381.971" -> 381971
Line 163: "389,841" -> 389841
Line 164: "434,044" -> 434044
Line 165: "016,613" -> 16613
Line 166: "444,800" -> 444800
Line 167: "438,000" -> 438000
Line 168: "508,082" -> 508082
Line 169: "506,489" -> 506489
Line 170: "Hides (All Kinde)" -> current = Hides (All Kinds)
Line 171: "304,418" -> 304418
Line 172: "197,481" -> 197481
Line 173: "A59,880" -> 59880? A59,880 likely 59,880
Line 174: "281,127" -> 281127
Line 175: "369,967" -> 369967
Line 176: "247.149" -> 247149
Line 177: "Leather (All Kinds)" -> current = Leather (All Kinds)
Line 178: "34,649" -> 34649
Line 179: "36,530" -> 36530
Line 180: "62,212" -> 62212
Line 181: "62.969" -> 62969
Line 182: "39,019" -> 39019
Line 183: "39,465" -> 39465
Line 184: "253,900 29,802" -> 253900, 29802
Line 185: "324,069 06,457" -> 324069, 6457
Line 186: "818,718 89,988" -> 818718, 89988
Line 187: "404,026 75,048" -> 404026, 75048
Line 188: "664,679 354,552 76,859" -> 664679, 354552, 76859
Line 189: "609.512" -> 609512
Line 190: "557.643" -> 557643
Line 191: "76.822" -> 76822
Line 192: "Matches & Match Making" -> current = Matches & Match Making Materials
Line 193: "Materiala" -> continuation
Line 194: "29,577" -> 29577
Line 195: "All Other Sundries" -> current = All Other Sundries
Line 196: "2.409,504" -> 2409504
Line 197: "70,365 2,432,977" -> 70365, 2432977
Line 198: "81,397 2,924,928" -> 81397, 2924928
Line 199: "54.884 2.577.246" -> 54884, 2577246
Line 200: "92,280 2,238,728" -> 92280, 2238728
Line 201: "73.449 3,158,557" -> 73449, 3158557
Line 202: "96,207 2,018,850" -> 96207, 2018850
Line 203: "109,881 2,768,001" -> 109881, 2768001
Line 204: "142,018 8.457.214" -> 142018, 8457214
Line 205: "119,998 9,538,683" -> 119998, 9538683
Line 206: "178,876 3,830,154" -> 178876, 3830154
Line 207: "99.858" -> 99858
Line 208: "4.989.385" -> 4989385
Line 209: "Total" -> current = Total
Line 210: "58.130.626" -> 58130626
Line 211: "31,758,970" -> 31758970
Line 212: "84.277,671" -> 84277671
Line 213: "87.997,894" -> 87997894
Line 214: "74,428,917" -> 74428917
Line 215: "35,498,899" -> 35498899
Line 216: "30.792.805" -> 30792805
Line 217: "86,245,411" -> 86245411
Line 218: "31,909.023" -> 31909023
Line 219: "98,604,522" -> 98604522
Line 220: "42.190,982" -> 42190982
Line 221: "38.914.710" -> 38914710
Now, for each category, I have a list of numbers. But many categories have far fewer than 12 numbers. For example, Animals, Live has 2 numbers. Building Materials has 0. Chemicals and Drugs has 7 numbers (220848, 294775, 284698, 271050, 14590, 269491, 208205) = 7. Chinese Medicines has many: let's count: 920717, 1084489, 1298581, 8300, 291624, 399487, 1281869, 18000, 360269, 198775, 17187, 337710, 272196, 989625 = 14 numbers. That's more than 12.
It seems the numbers are not separated per category correctly. The OCR likely read the table column by column, so the numbers for all categories for January are together, then February, etc. But the OCR output is row-major? Actually, the total row at the end has 12 numbers in order, which matches the months. So the table is row-major: each row has 12 numbers.
But the OCR has broken each row into multiple lines. So for each category, the numbers are in the lines immediately
Articles
January
February
(816)
TOTAL VALUES OF EXPORTS FOR THE COMPLETED MONTHS OF 1938 BY MAIN GROUPS.
March
April
May
June
July
August
September
October
November
Decernber
$
Animals, Live
7,800
8,622
Building Materials
Chemicals and Drugs...
220,848 294,775
284,698 271,050
14,590 269,491 208,205
Chinese Medicines
920,717
1.084.489
1.298.581
8,300 291.624 399.487 1.281.869
18,000 360,269 198,775
17,187 337,710 272,196
989.625
Dyeing & Tanning Materiala
299,799
Foodstuffs & Provisions
6,348,024
311.713 6.648.868
843,358
262,108
172,122
982,818 192,775
7,207,580
Fuels
21,148
18,946
36,487
Hardware
Liquor, Intoxicating
Machinery 3 Engines.
Manurea
Metala
TLA
Minerais & Ores
Nuts & Seeds
152,982 52,580 1.505.081 378,769 2,890,692
305,219 368,878
144,086
279,812
87,976
120,159
1.195.257 427,291 3,088.542
878,931
698.496 3.706,187
7,845,902 31,426 202,579 68.485 579,709 792,788 3,241,879
9,426,108
16,187 201,599 86,182
9,897,879 239,876 207,618
10,500 316,504 231,768 1,141,782 177,920 6,692,069
25,986
79,400
478,826
3,107,955
2,701,408
Oils & Fats
Painte
Paper & Paperware
Radway Materials
Tobacco
Treasure
Vehicles
2,156,242
241.277 2.446,565
449,708 302,588 2,444,092
548,808 298.994 2,416,970
1,048,891 287,168 2,426,840
117,987 480,251
Piece Goods & Textilen
2,878,087
118,948 607,766 2,947,809
187,589 208,579
29.701 360,662
34.199,999
6.888,826
130,978 890,971 3,454.598 606,249 646,452 6,794,679
221,414 564,149 8,592,880
140,209 984,118 10,958,440
134.521
110,947
Weaning Apparel..
Sundries:-
Bags
920,719
481,912
200,511 990,490
164,850 918,300
988,665 784,772
69.888 789,999 9,776,061 32.044 407,901 44,452,115
298,869 889, 182 2,186,208 982,969 242,782 2,654,867 84,455 543,887 8,109,909 18.405 287,215 5,640.434 1.995,265
1,316,592 308,974 3,384,501 82,356 708,588 2,481,209 1,550 242,807 19,612,903 854.418
151,155 57,066 552,885 695,320 2.377,889
9,060 220,152 216.426 1,304,499 240,079 7,199,188 25,508 201,270 41.817 91,115 1,905,005 2,561,059 911,439 427.113 3,014,929 88,468 565,508 2,236,852 804 278,164 10,745,369
949,697
982.809
160,269 #72,856
18,870 272,966 327,921 1,815,098 431,812 7,681,004 65.852 389,908 70,824 1,121,826 1,562,710 2,925,569 524,611 961,785 2,054,195 124,072 598,759 9,556,549 456 288,607 2,064,618 284,268 1,078,845
10.260 318.894 994,242 1,434,459 454,986 7,723.014 46.392 350.443 71.241 1,000,014 368,659 3,180,47% 908,765 871.010 3,616,886 123,458 688,907 4,482.950
26.001 372,498 8,877,817
281,925 1,478,500
11,240 345.963 322,871 1,250,757 408,490
9.480 948.760
386,947
942,052
401,060
7,385,860 37,904 411,483 88,502 1,891,436 248,842 4,075,954 818.046 487.126 3,520,812 198,016 785.779 4,642,880 113,424 385,449
7.929.398
42,248
879.267
72,227 358.786
311,791 5,146,749
523,026
404.683
3,745,425 3,818,799 1.449.789
8.974.511
143,210 17.899 4.0159.653
14.166
433,606 4.131.125 376.899 1,487,748
274.484
508,683
299,246
221,617
173,215
198.858
179,182
222.370
407,151
612.360
908,200
454.539
Electrical Apparatus..
288,722
321,014
381.971
389,841
434,044
016,613
444,800
438,000
508,082
506,489
Hides (All Kinde)
304,418
197,481
A59,880
281,127
369,967
247.149
Leather (All Kinds)
34,649
36,530
62,212
62.969
39,019
39,465
253,900 29,802
324,069 06,457
818,718 89,988
404,026 75,048
664,679 354,552 76,859
609.512
557.643
76.822
Matches & Match Making
Materiala
29,577
All Other Sundries
2.409,504
70,365 2,432,977
81,397 2,924,928
54.884 2.577.246
92,280 2,238,728
73.449 3,158,557
96,207 2,018,850
109,881 2,768,001
142,018 8.457.214
119,998 9,538,683
178,876 3,830,154
99.858
4.989.385
Total
58.130.626
31,758,970
84.277,671
87.997,894
74,428,917
35,498,899
30.792.805
86,245,411
31,909.023
98,604,522
42.190,982
38.914.710
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