1931 — Page 432

The user wants me to proofread OCR output of a historical table showing monthly export values by main groups for Hong Kong. The data appears to be a table with months as columns and commodity groups as rows. The OCR has many issues: broken numbers, misaligned columns, missing headers, garbled text.

I need to reconstruct this as a proper Markdown table. Let me analyze the structure:

The first line shows "430" then "Articles" then months January through December. Then "(512)" and "TOTAL MONTHLY VALUES OF EXPORTS BY MAIN GROUPS." Then months again? Actually looks like the header row got duplicated.

The data rows have commodity groups and 12 monthly values each. Some rows are split across lines. Numbers have OCR errors: spaces in numbers, wrong decimal separators (commas vs periods), missing digits, garbled characters.

I need to:

1. Identify the 12 month columns: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec
2. Identify each commodity group row
3. Clean up numbers: remove spaces, fix decimal separators (Hong Kong likely uses commas for thousands, periods for decimals? But these look like whole numbers with commas as thousand separators)
4. Handle missing/garbled data with ...

Let me parse row by row.

First, the header: The months appear twice. The first line: "January February March April May June July August September October November December" - that's 12 months. Then "(512) TOTAL MONTHLY VALUES OF EXPORTS BY MAIN GROUPS." Then months again? Actually the next line shows "April May June July August September October November December" but missing Jan-Mar? Wait, the OCR shows:

"January February March April May June July August September October November December & $ Anituals, Live 35.361 35,726 43,136 37,906 36.653 46,780 40.097 41,129 32.039 40,650 29,970 34.352"

So the header row is months Jan-Dec. Then a row for "Animals, Live" with 12 numbers.

But the OCR has "& $" on a line - probably "Unit: $" or something.

Let me list all commodity groups I can see:

1. Animals, Live
2. Building Materials
3. Chemicals and Drugs
4. Chinese Medicines
5. Dyeing & Tanning Materials
6. Foodstuffs & Provisions
7. Fuels
8. Hardware
9. Liquor, Intoxicating
10. Machinery & Engines
11. Manures
12. Metals
13. Minerals & Ores
14. Nuts & Seeds
15. Oil & Fats
16. Paints
17. Paper & Paperware
18. Piece Goods & Textiles
19. Railway Materials
20. Tobacco
21. Treasure
22. Vehicles
23. Wearing Apparel
24. Sundries: Bags
25. Electric Apparatus
26. Hides (All Kinds)
27. Leather (All Kinds)
28. Matches & Match Making Material
29. All Other Sundries
30. Total

Some rows are split across multiple lines in OCR. Need to reconstruct each row with 12 monthly values.

Let me go through the text systematically.

The text after "December" shows "& $" then "Anituals, Live" (should be "Animals, Live") then 12 numbers: 35.361 35,726 43,136 37,906 36.653 46,780 40.097 41,129 32.039 40,650 29,970 34.352

Note: numbers use both commas and periods as thousand separators? 35.361 likely 35,361. 35,726 is 35,726. Inconsistent. Probably all should be commas for thousands. But 35.361 could be 35.361 (decimal) but unlikely for export values. Likely OCR misread commas as periods. I'll standardize to commas for thousands.

Next: "Building Materials 684.017 543,340 764,344 $40.690 736.172 921,710 787.744 G84.006 744.066 850,592 696,200 1.115,709"

Issues: "$40.690" probably 840,690? Or 640,690? "G84.006" probably 684,006? "1.115,709" likely 1,115,709.

Next: "Chemicals and Drugs 363,984 Chinese Medicines. 1.373.807 291,941 1,190.743 430.702 355,010 421.366 544,125 399,115 363,890 358,872 343,915 383,876 1,990,100 1.426,744 1.222,401 1,643,717 1,407,707 1,343 562 1,328.933 1,445.474 1,078,169"

This seems like two rows merged: "Chemicals and Drugs" and "Chinese Medicines". The numbers: for Chemicals and Drugs only one number "363,984" then Chinese Medicines starts with "1.373.807". But there should be 12 numbers each. Probably the OCR missed line breaks. Let's see: after "363,984" the next numbers belong to Chinese Medicines? But there are many numbers. Actually maybe "Chemicals and Drugs" has 12 numbers but they are split. Let's count numbers after "Chemicals and Drugs": 363,984 (1), then Chinese Medicines label, then 1.373.807 (2), 291,941 (3), 1,190.743 (4), 430.702 (5), 355,010 (6), 421.366 (7), 544,125 (8), 399,115 (9), 363,890 (10), 358,872 (11), 343,915 (12), 383,876 (13), 1,990,100 (14), 1.426,744 (15), 1.222,401 (16), 1,643,717 (17), 1,407,707 (18), 1,343 562 (19), 1,328.933 (20), 1,445.474 (21), 1,078,169 (22). That's 22 numbers for two rows? 12 each would be 24. Missing two.

Maybe "Chemicals and Drugs" has 12 numbers but the first is 363,984 and the next 11 are the first 11 of the list? But then Chinese Medicines would have 12 starting from 383,876? Let's see: 383,876, 1,990,100, 1.426,744, 1.222,401, 1,643,717, 1,407,707, 1,343 562, 1,328.933, 1,445.474, 1,078,169 - that's 10 numbers. Not 12.

Alternatively, the table might have more than 12 columns? But header says 12 months.

Let's look at the total row at the end: "Total 58,821,617 48,508,919 64,065,634 53,106,381 49,928,326 58,612,373 57,742,888 59,891,420 69,133,965 55,983,957 48,328,972 49,400,267" That's 12 numbers. Good.

So each row must have 12 numbers.

I need to parse the OCR text as a sequence of tokens and assign to rows.

Better approach: The OCR output is essentially a linearized table. I can write a script mentally, but here I'll do manually.

Let me copy the text into a more readable format.

The user provided a block of text. I'll split by lines as given.

Lines:

1. "430"
2. ""
3. "Articles"
4. ""
5. "January"
6. ""
7. "February"
8. ""
9. "March"
10. ""
11. "(512)"
12. ""
13. "TOTAL MONTHLY VALUES OF EXPORTS BY MAIN GROUPS."
14. ""
15. "April"
16. ""
17. "May"
18. ""
19. "June"
20. ""
21. "July"
22. ""
23. "August"
24. ""
25. "September"
26. ""
27. "October"
28. ""
29. "November"
30. ""
31. "December"
32. ""
33. "&"
34. ""
35. "$"
36. ""
37. "Anituals, Live"
38. ""
39. "35.361"
40. ""
41. "35,726"
42. ""
43. "43,136"
44. ""
45. "37,906"
46. ""
47. "36.653"
48. ""
49. "46,780"
50. ""
51. "40.097"
52. ""
53. "41,129"
54. ""
55. "32.039"
56. ""
57. "40,650"
58. ""
59. "29,970"
60. ""
61. "34.352"
62. ""
63. "Building Materials"
64. ""
65. "684.017"
66. ""
67. "543,340"
68. ""
69. "764,344"
70. ""
71. "$40.690"
72. ""
73. "736.172"
74. ""
75. "921,710"
76. ""
77. "787.744"
78. ""
79. "G84.006"
80. ""
81. "744.066"
82. ""
83. "850,592"
84. ""
85. "696,200"
86. ""
87. "1.115,709"
88. ""
89. "Chemicals and Drugs"
90. ""
91. "363,984"
92. ""
93. "Chinese Medicines."
94. ""
95. "1.373.807"
96. ""
97. "291,941"
98. ""
99. "1,190.743"
100. ""
101. "430.702"
102. ""
103. "355,010"
104. ""
105. "421.366"
106. ""
107. "544,125"
108. ""
109. "399,115"
110. ""
111. "363,890"
112. ""
113. "358,872"
114. ""
115. "343,915"
116. ""
117. "383,876"
118. ""
119. "1,990,100"
120. ""
121. "1.426,744"
122. ""
123. "1.222,401"
124. ""
125. "1,643,717"
126. ""
127. "1,407,707"
128. ""
129. "1,343 562"
130. ""
131. "1,328.933"
132. ""
133. "1,445.474"
134. ""
135. "1,078,169"
136. ""
137. "Dyeing & Tanning Materials."
138. ""
139. "583.222"
140. ""
141. "Foodstuffs & Provisions"
142. ""
143. "18,501,578"
144. ""
145. "486,597"
146. ""
147. "12,722,501"
148. ""
149. "879,769"
150. ""
151. "493.150"
152. ""
153. "490,674"
154. ""
155. "506,365"
156. ""
157. "468,768"
158. ""
159. "409,835"
160. ""
161. "18.277.466"
162. ""
163. "17.407,595"
164. ""
165. "18,796,924"
166. ""
167. "17,554,945"
168. ""
169. "16,758,202"
170. ""
171. "15,551,768"
172. ""
173. "496,459"
174. ""
175. "14,574,700"
176. ""
177. "501,185"
178. ""
179. "653,427"
180. ""
181. "351,941"
182. ""
183. "1,172.576"
184. ""
185. "521 105"
186. ""
187. "15.842,604 | 17,418,42%"
188. ""
189. "17 6:10"
190. ""
191. "Fuels"
192. ""
193. "277.362"
194. ""
195. "204.994"
196. ""
197. "255,073"
198. ""
199. "223.745"
200. ""
201. "261,713"
202. ""
203. "250,337"
204. ""
205. "232,686"
206. ""
207. "219,465"
208. ""
209. "212.447"
210. ""
211. "318,506"
212. ""
213. "301,268"
214. ""
215. "221,311"
216. ""
217. "Hardware"
218. ""
219. "346,793"
220. ""
221. "176,085"
222. ""
223. "241,320"
224. ""
225. "283,704"
226. ""
227. "267,634"
228. ""
229. "244.009"
230. ""
231. "214,648"
232. ""
233. "219,932"
234. ""
235. "303,467"
236. ""
237. "251,410"
238. ""
239. "202,941"
240. ""
241. "237,872"
242. ""
243. "Liquor, Intoxicating"
244. ""
245. "215,832"
246. ""
247. "125.385"
248. ""
249. "123,339"
250. ""
251. "125.644"
252. ""
253. "122,575"
254. ""
255. "121,524"
256. ""
257. "156.975"
258. ""
259. "61,048"
260. ""
261. "158.487"
262. ""
263. "$5,244"
264. ""
265. "120,464"
266. ""
267. "116,000"
268. ""
269. "Machinery & Engines..."
270. ""
271. "97,184"
272. ""
273. "61,052"
274. ""
275. "99,307"
276. ""
277. "177,221"
278. ""
279. "175,535"
280. ""
281. "185,001"
282. ""
283. "469.174"
284. ""
285. "236,167"
286. ""
287. "108,748"
288. ""
289. "207.501"
290. ""
291. "101,560"
292. ""
293. "222.906"
294. ""
295. "Manures"
296. ""
297. "735.069"
298. ""
299. "Metals"
300. ""
301. "3,839,944"
302. ""
303. "Minerals & Ores..."
304. ""
305. "102,701"
306. ""
307. "873,106"
308. ""
309. "3,674,341"
310. ""
311. "297,945"
312. ""
313. "1.979,714 1,789,877"
314. ""
315. "4,893,440"
316. ""
317. "765,399"
318. ""
319. "1,410,785"
320. ""
321. "2,907,453"
322. ""
323. "2,286,521"
324. ""
325. "2,337,700"
326. ""
327. "282,970"
328. ""
329. "317,831"
330. ""
331. "226,314"
332. ""
333. "Nuts & Seeds"
334. ""
335. "759,013"
336. ""
337. "436.423"
338. ""
339. "301,542"
340. ""
341. "407,413"
342. ""
343. "426,295"
344. ""
345. "94,728"
346. ""
347. "440,945"
348. ""
349. "IFF"
350. ""
351. "Oil & Fats"
352. ""
353. "Paints"
354. ""
355. "Paper & Paperware"
356. ""
357. "4,263,629"
358. ""
359. "3,834,369"
360. ""
361. "3,678,672"
362. ""
363. "2,966.587"
364. ""
365. "3,166,904"
366. ""
367. "3,113,480"
368. ""
369. "43.793"
370. ""
371. "113.939"
372. ""
373. "2,901,140"
374. ""
375. "503,604 1,067,308"
376. ""
377. "2,107,617 2,045,873"
378. ""
379. "364.990"
380. ""
381. "531,420"
382. ""
383. "5,473.108"
384. ""
385. "2,028,136"
386. ""
387. "3.314.331"
388. ""
389. "1,582,741"
390. ""
391. "206,158"
392. ""
393. "2,438,263"
394. ""
395. "3,220.099"
396. ""
397. "2,058,503"
398. ""
399. "2.730.055"
400. ""
401. "47.101"
402. ""
403. "51,410"
404. ""
405. "$8,816"
406. ""
407. "42,631"
408. ""
409. "575,217"
410. ""
411. "512.322"
412. ""
413. "663,521"
414. ""
415. "621.155"
416. ""
417. "2,075,098"
418. ""
419. "4,029,406"
420. ""
421. "3,855,081"
422. ""
423. "4,150,422"
424. ""
425. "267,925"
426. ""
427. "140,996"
428. ""
429. "818,439"
430. ""
431. "172,957"
432. ""
433. "194,136"
434. ""
435. "239,732"
436. ""
437. "165,$77"
438. ""
439. "215,906"
440. ""
441. "223,403"
442. ""
443. "252,789"
444. ""
445. "243,115"
446. ""
447. "192,684"
448. ""
449. "1,076,663"
450. ""
451. "Picece Goods & Textiles"
452. ""
453. "5,214,753"
454. ""
455. "Railway Materials"
456. ""
457. "Tobacco"
458. ""
459. "49,036"
460. ""
461. "773,007"
462. ""
463. "Treasure"
464. ""
465. "10,845,988"
466. ""
467. "Vehicles"
468. ""
469. "544,977"
470. ""
471. "Wearing Apparel"
472. ""
473. "1.207,308"
474. ""
475. "732,696"
476. ""
477. "4,613,657"
478. ""
479. "16,573"
480. ""
481. "554,891"
482. ""
483. "10,787,052"
484. ""
485. "63,823"
486. ""
487. "691,644"
488. ""
489. "1.130,232"
490. ""
491. "827,946"
492. ""
493. "8,595,942 6,571,470 4,537,732"
494. ""
495. "35,990"
496. ""
497. "64,564"
498. ""
499. "66,823"
500. ""
501. "817,660"
502. ""
503. "936,964 733,358"
504. ""
505. "9,631,384 7,374,647 7.279,292"
506. ""
507. "86,021"
508. ""
509. "120,902 141,321"
510. ""
511. "1.414,033 1,452,616 1,247,728"
512. ""
513. "919,994"
514. ""
515. "1,110,223"
516. ""
517. "1,046,578"
518. ""
519. "1,110,106"
520. ""
521. "988,060"
522. ""
523. "989,080"
524. ""
525. "684,972"
526. ""
527. "752,588"
528. ""
529. "5,699,859"
530. ""
531. "5.931,030"
532. ""
533. "37,193"
534. ""
535. "773,813"
536. ""
537. "14,756,630"
538. ""
539. "42,417"
540. ""
541. "965,8GO"
542. ""
543. "16,139,474"
544. ""
545. "8,558,248"
546. ""
547. "50,789"
548. ""
549. "646,894"
550. ""
551. "15,831,547"
552. ""
553. "247,289"
554. ""
555. "162,799"
556. ""
557. "208,631"
558. ""
559. "1,020,060"
560. ""
561. "913,423"
562. ""
563. "859,490"
564. ""
565. "9,043,862"
566. ""
567. "14.274"
568. ""
569. "600.104"
570. ""
571. "14,153,078"
572. ""
573. "119,861"
574. ""
575. "1.091.067"
576. ""
577. "6,831,164"
578. ""
579. "5,660"
580. ""
581. "1.187,816"
582. ""
583. "6,519.251"
584. ""
585. "149,169"
586. ""
587. "1.224,639"
588. ""
589. "5.980.917"
590. ""
591. "8,820"
592. ""
593. "1.079.438"
594. ""
595. "3,106,138"
596. ""
597. "4,230,907"
598. ""
599. "48,378"
600. ""
601. "1,002,881"
602. ""
603. "138,765"
604. ""
605. "1,225,438"
606. ""
607. "5,349,399"
608. ""
609. "148,911"
610. ""
611. "1,162,532"
612. ""
613. "Sundries:-"
614. ""
615. "Bags"
616. ""
617. "979,225"
618. ""
619. "1,501,444 1.921,307"
620. ""
621. "837,047"
622. ""
623. "814,276"
624. ""
625. "518,553"
626. ""
627. "618,186"
628. ""
629. "917,617"
630. ""
631. "1,054,018"
632. ""
633. "1,882,179 1,048,105"
634. ""
635. "2,032,793"
636. ""
637. "Electric Apparatus"
638. ""
639. "371,759"
640. ""
641. "347,236"
642. ""
643. "463,312"
644. ""
645. "414,470"
646. ""
647. "437,312"
648. ""
649. "499,872"
650. ""
651. "300.309"
652. ""
653. "105,862"
654. ""
655. "449.231"
656. ""
657. "482,457"
658. ""
659. "377,940"
660. ""
661. "284.483"
662. ""
663. "Hides (All Kinds) ..."
664. ""
665. "376,666"
666. ""
667. "249,602"
668. ""
669. "411.017"
670. ""
671. "329,563"
672. ""
673. "302,920"
674. ""
675. "325,878"
676. ""
677. "209,233"
678. ""
679. "277,184"
680. ""
681. "294,265"
682. ""
683. "84,803"
684. ""
685. "150,421"
686. ""
687. "104,789"
688. ""
689. "Leather (All Kinds)"
690. ""
691. "183,558"
692. ""
693. "107,644"
694. ""
695. "242,973"
696. ""
697. "189,947"
698. ""
699. "145,477"
700. ""
701. "193,330"
702. ""
703. "190,877"
704. ""
705. "156,980"
706. ""
707. "225,775"
708. ""
709. "348,549"
710. ""
711. "201,759"
712. ""
713. "233,663"
714. ""
715. "Matches & Match Making"
716. ""
717. "Material"
718. ""
719. "H"
720. ""
721. "334,788"
722. ""
723. "182,862"
724. ""
725. "311,153"
726. ""
727. "301,385"
728. ""
729. "250,029"
730. ""
731. "287,331"
732. ""
733. "319,104"
734. ""
735. "410,527"
736. ""
737. "450,993"
738. ""
739. "350,375"
740. ""
741. "229,716"
742. ""
743. "230.383"
744. ""
745. "All Other Sundries"
746. ""
747. "4,415,778"
748. ""
749. "3,335,245 4,355,259"
750. ""
751. "3,968,267"
752. ""
753. "3,546,907 3,576,469"
754. ""
755. "3.549.166"
756. ""
757. "3,532.054"
758. ""
759. "3,950,871"
760. ""
761. "3.668,371 3,816,403 4,183,044"
762. ""
763. "Total"
764. ""
765. "58,821,617"
766. ""
767. "48,508,919"
768. ""
769. "64,065,634"
770. ""
771. "53,106,381"
772. ""
773. "49,928,326"
774. ""
775. "58,612,373"
776. ""
777. "57,742,888"
778. ""
779. "59,891,420"
780. ""
781. "69,133,965"
782. ""
783. "55,983,957"
784. ""
785. "48,328,972"
786. ""
787. "49,400,267"

This is a mess. The OCR has inserted blank lines between every token. But we can ignore blank lines and treat as a sequence.

Better to parse the original text as a continuous stream. But given the user pasted a block, I need to reconstruct the table.

Given the complexity, I'll write a Markdown table with the rows I can identify, cleaning numbers.

I'll assume the columns are: Article, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec.

From the total row, the 12 numbers correspond to months Jan-Dec.

Now, let's assign each commodity group 12 numbers in order of appearance.

I'll go through the token list sequentially, grouping by known commodity names.

Known commodity names (from the text):

• Animals, Live
• Building Materials
• Chemicals and Drugs
• Chinese Medicines
• Dyeing & Tanning Materials
• Foodstuffs & Provisions
• Fuels
• Hardware
• Liquor, Intoxicating
• Machinery & Engines
• Manures
• Metals
• Minerals & Ores
• Nuts & Seeds
• Oil & Fats
• Paints
• Paper & Paperware
• Piece Goods & Textiles
• Railway Materials
• Tobacco
• Treasure
• Vehicles
• Wearing Apparel
• Sundries: Bags
• Electric Apparatus
• Hides (All Kinds)
• Leather (All Kinds)
• Matches & Match Making Material
• All Other Sundries
• Total

Some names are split: "Matches & Match Making" and "Material" separate. "Picece Goods & Textiles" should be "Piece Goods & Textiles". "Anituals" -> "Animals". "IFF" likely a glitch.

Also "Sundries:-" then "Bags" separate.

Now, the numbers: after each commodity name, there should be 12 numbers. But the OCR splits numbers across lines and sometimes merges two numbers on one line (e.g., "1.979,714 1,789,877").

I'll need to extract 12 numbers per row.

Let's do it row by row using the token list.

I'll write a parser in my mind.

Start after the header tokens. The header tokens: "430", "Articles", "January", "February", "March", "(512)", "TOTAL MONTHLY VALUES OF EXPORTS BY MAIN GROUPS.", "April", "May", "June", "July", "August", "September", "October", "November", "December", "&", "$". Then data starts.

So data tokens start at "Anituals, Live".

I'll iterate through tokens, when I see a known commodity name (or something that looks like a label), start a new row, then collect next 12 numeric tokens.

But some commodity names are multi-word. I'll use a list of expected row labels.

Let's list expected row labels in order as they appear in the text:

1. Animals, Live
2. Building Materials
3. Chemicals and Drugs
4. Chinese Medicines
5. Dyeing & Tanning Materials
6. Foodstuffs & Provisions
7. Fuels
8. Hardware
9. Liquor, Intoxicating
10. Machinery & Engines
11. Manures
12. Metals
13. Minerals & Ores
14. Nuts & Seeds
15. Oil & Fats
16. Paints
17. Paper & Paperware
18. Piece Goods & Textiles
19. Railway Materials
20. Tobacco
21. Treasure
22. Vehicles
23. Wearing Apparel
24. Bags (under Sundries)
25. Electric Apparatus
26. Hides (All Kinds)
27. Leather (All Kinds)
28. Matches & Match Making Material
29. All Other Sundries
30. Total

Now, the tokens between labels are numbers. But there are also stray tokens like "IFF", "H", "Material", "Sundries:-", etc.

I'll go through the token list (non-blank) and assign.

Token list (non-blank) from above (line numbers refer to my numbered list above, but I'll just use the sequence):

1. 430
2. Articles
3. January
4. February
5. March
6. (512)
7. TOTAL MONTHLY VALUES OF EXPORTS BY MAIN GROUPS.
8. April
9. May
10. June
11. July
12. August
13. September
14. October
15. November
16. December
17. &
18. $
19. Anituals, Live
20. 35.361
21. 35,726
22. 43,136
23. 37,906
24. 36.653
25. 46,780
26. 40.097
27. 41,129
28. 32.039
29. 40,650
30. 29,970
31. 34.352
32. Building Materials
33. 684.017
34. 543,340
35. 764,344
36. $40.690
37. 736.172
38. 921,710
39. 787.744
40. G84.006
41. 744.066
42. 850,592
43. 696,200
44. 1.115,709
45. Chemicals and Drugs
46. 363,984
47. Chinese Medicines.
48. 1.373.807
49. 291,941
50. 1,190.743
51. 430.702
52. 355,010
53. 421.366
54. 544,125
55. 399,115
56. 363,890
57. 358,872
58. 343,915
59. 383,876
60. 1,990,100
61. 1.426,744
62. 1.222,401
63. 1,643,717
64. 1,407,707
65. 1,343 562
66. 1,328.933
67. 1,445.474
68. 1,078,169
69. Dyeing & Tanning Materials.
70. 583.222
71. Foodstuffs & Provisions
72. 18,501,578
73. 486,597
74. 12,722,501
75. 879,769
76. 493.150
77. 490,674
78. 506,365
79. 468,768
80. 409,835
81. 18.277.466
82. 17.407,595
83. 18,796,924
84. 17,554,945
85. 16,758,202
86. 15,551,768
87. 496,459
88. 14,574,700
89. 501,185
90. 653,427
91. 351,941
92. 1,172.576
93. 521 105
94. 15.842,604 | 17,418,42%
95. 17 6:10
96. Fuels
97. 277.362
98. 204.994
99. 255,073
100. 223.745
101. 261,713
102. 250,337
103. 232,686
104. 219,465
105. 212.447
106. 318,506
107. 301,268
108. 221,311
109. Hardware
110. 346,793
111. 176,085
112. 241,320
113. 283,704
114. 267,634
115. 244.009
116. 214,648
117. 219,932
118. 303,467
119. 251,410
120. 202,941
121. 237,872
122. Liquor, Intoxicating
123. 215,832
124. 125.385
125. 123,339
126. 125.644
127. 122,575
128. 121,524
129. 156.975
130. 61,048
131. 158.487
132. $5,244
133. 120,464
134. 116,000
135. Machinery & Engines...
136. 97,184
137. 61,052
138. 99,307
139. 177,221
140. 175,535
141. 185,001
142. 469.174
143. 236,167
144. 108,748
145. 207.501
146. 101,560
147. 222.906
148. Manures
149. 735.069
150. Metals
151. 3,839,944
152. Minerals & Ores...
153. 102,701
154. 873,106
155. 3,674,341
156. 297,945
157. 1.979,714 1,789,877
158. 4,893,440
159. 765,399
160. 1,410,785
161. 2,907,453
162. 2,286,521
163. 2,337,700
164. 282,970
165. 317,831
166. 226,314
167. Nuts & Seeds
168. 759,013
169. 436.423
170. 301,542
171. 407,413
172. 426,295
173. 94,728
174. 440,945
175. IFF
176. Oil & Fats
177. Paints
178. Paper & Paperware
179. 4,263,629
180. 3,834,369
181. 3,678,672
182. 2,966.587
183. 3,166,904
184. 3,113,480
185. 43.793
186. 113.939
187. 2,901,140
188. 503,604 1,067,308
189. 2,107,617 2,045,873
190. 364.990
191. 531,420
192. 5,473.108
193. 2,028,136
194. 3.314.331
195. 1,582,741
196. 206,158
197. 2,438,263
198. 3,220.099
199. 2,058,503
200. 2.730.055
201. 47.101
202. 51,410
203. $8,816
204. 42,631
205. 575,217
206. 512.322
207. 663,521
208. 621.155
209. 2,075,098
210. 4,029,406
211. 3,855,081
212. 4,150,422
213. 267,925
214. 140,996
215. 818,439
216. 172,957
217. 194,136
218. 239,732
219. 165,$77
220. 215,906
221. 223,403
222. 252,789
223. 243,115
224. 192,684
225. 1,076,663
226. Picece Goods & Textiles
227. 5,214,753
228. Railway Materials
229. Tobacco
230. 49,036
231. 773,007
232. Treasure
233. 10,845,988
234. Vehicles
235. 544,977
236. Wearing Apparel
237. 1.207,308
238. 732,696
239. 4,613,657
240. 16,573
241. 554,891
242. 10,787,052
243. 63,823
244. 691,644
245. 1.130,232
246. 827,946
247. 8,595,942 6,571,470 4,537,732
248. 35,990
249. 64,564
250. 66,823
251. 817,660
252. 936,964 733,358
253. 9,631,384 7,374,647 7.279,292
254. 86,021
255. 120,902 141,321
256. 1.414,033 1,452,616 1,247,728
257. 919,994
258. 1,110,223
259. 1,046,578
260. 1,110,106
261. 988,060
262. 989,080
263. 684,972
264. 752,588
265. 5,699,859
266. 5.931,030
267. 37,193
268. 773,813
269. 14,756,630
270. 42,417
271. 965,8GO
272. 16,139,474
273. 8,558,248
274. 50,789
275. 646,894
276. 15,831,547
277. 247,289
278. 162,799
279. 208,631
280. 1,020,060
281. 913,423
282. 859,490
283. 9,043,862
284. 14.274
285. 600.104
286. 14,153,078
287. 119,861
288. 1.091.067
289. 6,831,164
290. 5,660
291. 1.187,816
292. 6,519.251
293. 149,169
294. 1.224,639
295. 5.980.917
296. 8,820
297. 1.079.438
298. 3,106,138
299. 4,230,907
300. 48,378
301. 1,002,881
302. 138,765
303. 1,225,438
304. 5,349,399
305. 148,911
306. 1,162,532
307. Sundries:-
308. Bags
309. 979,225
310. 1,501,444 1.921,307
311. 837,047
312. 814,276
313. 518,553
314. 618,186
315. 917,617
316. 1,054,018
317. 1,882,179 1,048,105
318. 2,032,793
319. Electric Apparatus
320. 371,759
321. 347,236
322. 463,312
323. 414,470
324. 437,312
325. 499,872
326. 300.309
327. 105,862
328. 449.231
329. 482,457
330. 377,940
331. 284.483
332. Hides (All Kinds) ...
333. 376,666
334. 249,602
335. 411.017
336. 329,563
337. 302,920
338. 325,878
339. 209,233
340. 277,184
341. 294,265
342. 84,803
343. 150,421
344. 104,789
345. Leather (All Kinds)
346. 183,558
347. 107,644
348. 242,973
349. 189,947
350. 145,477
351. 193,330
352. 190,877
353. 156,980
354. 225,775
355. 348,549
356. 201,759
357. 233,663
358. Matches & Match Making
359. Material
360. H
361. 334,788
362. 182,862
363. 311,153
364. 301,385
365. 250,029
366. 287,331
367. 319,104
368. 410,527
369. 450,993
370. 350,375
371. 229,716
372. 230.383
373. All Other Sundries
374. 4,415,778
375. 3,335,245 4,355,259
376. 3,968,267
377. 3,546,907 3,576,469
378. 3.549.166
379. 3,532.054
380. 3,950,871
381. 3.668,371 3,816,403 4,183,044
382. Total
383. 58,821,617
384. 48,508,919
385. 64,065,634
386. 53,106,381
387. 49,928,326
388. 58,612,373
389. 57,742,888
390. 59,891,420
391. 69,133,965
392. 55,983,957
393. 48,328,972
394. 49,400,267

Now, I need to assign 12 numbers to each row label.

Row labels in order of appearance (with token index):

• Anituals, Live (19)
• Building Materials (32)
• Chemicals and Drugs (45)
• Chinese Medicines. (47)
• Dyeing & Tanning Materials. (69)
• Foodstuffs & Provisions (71)
• Fuels (96)
• Hardware (109)
• Liquor, Intoxicating (122)
• Machinery & Engines... (135)
• Manures (148)
• Metals (150)
• Minerals & Ores... (152)
• Nuts & Seeds (167)
• Oil & Fats (176) - but note token 175 is "IFF", 176 "Oil & Fats", 177 "Paints", 178 "Paper & Paperware". Then numbers start at 179. So likely three rows: Oil & Fats, Paints, Paper & Paperware. But they are not separated by numbers? The numbers after 178 are for these three rows? Let's see: tokens 179 onward are numbers. There are many numbers. Probably each of these three rows has 12 numbers. But the numbers are interleaved? Actually the OCR likely lost row boundaries. We have to split the number sequence into three rows of 12 each.

Similarly, after "Picece Goods & Textiles" (226) we have "5,214,753" then "Railway Materials" (228) then "Tobacco" (229) then numbers. So Railway Materials and Tobacco are separate rows.

After "Treasure" (232) number 10,845,988 then "Vehicles" (234) number 544,977 then "Wearing Apparel" (236) then numbers.

Then after "Wearing Apparel" we have many numbers that likely belong to multiple rows? But the next labels are "Sundries:-", "Bags", "Electric Apparatus", "Hides (All Kinds) ...", "Leather (All Kinds)", "Matches & Match Making", "Material", "H", "All Other Sundries", "Total". However, the numbers between Wearing Apparel and Sundries:- are many. Let's see: after token 236 (Wearing Apparel), tokens 237-306 are numbers, then token 307 Sundries:-. That's 70 tokens, but many are multi-number tokens. We need to assign 12 numbers to Wearing Apparel, then maybe there are other rows not labeled? But the table likely includes all rows listed. The labels after Wearing Apparel are Sundries:-, Bags, Electric Apparatus, Hides, Leather, Matches & Match Making Material, All Other Sundries. That's 6 rows (including Bags as separate). But there are also "Railway Materials", "Tobacco", "Treasure", "Vehicles" before Wearing Apparel. So total rows: let's count expected rows from the label list: 30 rows including Total. We have 30 labels. Good.

So each label gets 12 numbers.

Now, we need to extract 12 numbers per label from the token stream.

We'll go label by label, taking the next 12 numeric tokens (where a numeric token may contain multiple numbers separated by spaces). We'll split multi-number tokens.

Define a function to get next 12 numbers from token stream after label.

Let's simulate.

Initialize token pointer at 19 (Anituals, Live). But we need to skip header tokens. Start at token 19.

We'll process labels in order of appearance in token stream.

Label 1: "Anituals, Live" at token 19. Next tokens 20-31 are 12 tokens, each appears to be a single number. Good.

Numbers:

1. 35.361 -> 35,361
2. 35,726
3. 43,136
4. 37,906
5. 36.653 -> 36,653
6. 46,780
7. 40.097 -> 40,097
8. 41,129
9. 32.039 -> 32,039
10. 40,650
11. 29,970
12. 34.352 -> 34,352

Label 2: "Building Materials" at token 32. Next tokens 33-44: 12 tokens.

33: 684.017 -> 684,017

34: 543,340

35: 764,344

36: $40.690 -> likely 840,690? Or 640,690? The $ sign might be a misread of 8? Or 6? Could be 840,690. But let's see pattern: other numbers are around 600k-900k. 840,690 plausible. Could also be 640,690. I'll assume 840,690? But the token is "$40.690". The $ might be a misread of '8'? In OCR, '8' can look like '$'? Not sure. Could be 840,690. I'll note as 840,690 but maybe keep as 840,690. However, the next token 37: 736.172 -> 736,172. 38: 921,710. 39: 787.744 -> 787,744. 40: G84.006 -> likely 684,006 (G misread as 6?). 41: 744.066 -> 744,066. 42: 850,592. 43: 696,200. 44: 1.115,709 -> 1,115,709.

So 12 numbers.

Label 3: "Chemicals and Drugs" at token 45. Next token 46: 363,984. Then token 47 is "Chinese Medicines." which is next label. So Chemicals and Drugs only has one number? That can't be. Perhaps the numbers for Chemicals and Drugs are the next 11 tokens after 363,984 but they are interleaved with Chinese Medicines label? Actually token 47 is a label, so Chemicals and Drugs only got one number. That suggests the OCR missed the other 11 numbers for Chemicals and Drugs, or they are placed elsewhere. But looking at the token stream, after 363,984, the next label is Chinese Medicines. So maybe Chemicals and Drugs is a sub-category? Or the table has a row "Chemicals and Drugs" with 12 numbers, but the OCR only captured the first? However, the total row includes all. We might need to infer missing numbers as "...".

But let's check the original text: "Chemicals and Drugs 363,984 Chinese Medicines. 1.373.807 291,941 1,190.743 430.702 355,010 421.366 544,125 399,115 363,890 358,872 343,915 383,876 1,990,100 1.426,744 1.222,401 1,643,717 1,407,707 1,343 562 1,328.933 1,445.474 1,078,169"

There are many numbers after Chinese Medicines. Perhaps "Chemicals and Drugs" and "Chinese Medicines" are two rows, but the numbers for Chemicals and Drugs are the first 12 numbers after its label, but the OCR didn't put line breaks. The numbers after "Chemicals and Drugs" might be: 363,984, then the next 11 numbers are the first 11 of the list that follows? But the list follows "Chinese Medicines." So maybe the numbers for Chemicals and Drugs are: 363,984, 1.373.807, 291,941, 1,190.743, 430.702, 355,010, 421.366, 544,125, 399,115, 363,890, 358,872, 343,915? That would be 12 numbers. Then Chinese Medicines would start at 383,876 and have 12 numbers: 383,876, 1,990,100, 1.426,744, 1.222,401, 1,643,717, 1,407,707, 1,343 562, 1,328.933, 1,445.474, 1,078,169, ... but that's only 10 numbers. Need 12. There are two more numbers? After 1,078,169 comes "Dyeing & Tanning Materials." So maybe Chinese Medicines has only 10 numbers? Unlikely.

Alternatively, the table might have a different structure: maybe "Chemicals and Drugs" is a group header and "Chinese Medicines" is a sub-item? But the total row suggests each row is a commodity.

Given the difficulty, I'll treat each label as a row and take the next 12 numeric tokens after the label, where numeric tokens are tokens that look like numbers (including those with commas, periods, spaces). But the token stream has labels interspersed. So for "Chemicals and Drugs", the next token is 363,984 (numeric), then next token is "Chinese Medicines." (non-numeric). So only one numeric token before next label. That means the OCR failed to capture the other 11 numbers for that row. I'll have to mark them as missing (...).

But maybe the numbers for Chemicals and Drugs are actually the 12 numbers that appear later? No.

Let's look at the overall pattern: The total row has 12 numbers. The sum of all rows should match total. But we can't verify.

Given the instructions: "Missing text — if a word or phrase is clearly missing due to OCR damage, insert ... in its place." So for missing numbers, I can put ....

But we have many numbers in the stream. Perhaps the rows are not in the order of labels? The labels might be row headers, and the numbers are in columns. The OCR read column by column? But the text seems row-major.

Another approach: The original table likely has 12 columns (months) and many rows. The OCR output is a messy linearization. The best we can do is to reconstruct the table as best as possible, aligning numbers to months based on the total row.

We have the total row with 12 clear numbers. We can use that as anchor.

We also have the first two rows (Animals, Building Materials) with 12 numbers each.

For the rest, we need to parse.

Given the time, I'll produce a Markdown table with the rows I can parse clearly, and for rows with missing/garbled data, I'll fill with ... or best guess.

But the user expects a proofread text in Markdown, presumably the whole table.

I'll write a script in my mind to parse the token stream with a state machine: when we see a known label, start a new row, then collect numbers until we have 12 or hit another label.

But the token stream has labels that are not in my list (like "IFF", "H", "Material", "Sundries:-"). I'll treat "Sundries:-" as part of "Bags" label? Actually "Sundries:-" then "Bags" likely means "Bags" is a subcategory under Sundries. But the table might have "Sundries" as a group with sub-rows: Bags, Electric Apparatus, Hides, Leather, Matches, All Other Sundries. In the token stream, "Sundries:-" appears at token 307, then "Bags" at 308, then numbers. Then "Electric Apparatus" at 319, etc. So each sub-row has its own numbers.

Thus, the rows after "Wearing Apparel" are: Bags, Electric Apparatus, Hides (All Kinds), Leather (All Kinds), Matches & Match Making Material, All Other Sundries. That's 6 rows.

But before that, we have rows: Railway Materials, Tobacco, Treasure, Vehicles, Wearing Apparel. That's 5 rows.

And before that: Piece Goods & Textiles (1 row), then maybe Oil & Fats, Paints, Paper & Paperware (3 rows), then Nuts & Seeds (1), Minerals & Ores (1), Metals (1), Manures (1), Machinery & Engines (1), Liquor (1), Hardware (1), Fuels (1), Foodstuffs (1), Dyeing (1), Chinese Medicines (1), Chemicals and Drugs (1), Building Materials (1), Animals (1). That's 18 rows. Plus the 6 sundries = 24, plus 5 = 29, plus Total = 30. Good.

Now, we need to assign 12 numbers to each of these 29 rows (excluding total). The token stream has many numeric tokens. Let's count numeric tokens (split multi-number tokens) from token 20 to 382 (before Total). We'll need to split tokens that contain multiple numbers.

But doing this manually for 29 rows is tedious but doable with careful accounting.

I'll write a parsing algorithm on paper.

First, define a list of row labels in order of appearance in token stream (with token index):

1. Animals, Live (19)
2. Building Materials (32)
3. Chemicals and Drugs (45)
4. Chinese Medicines (47)
5. Dyeing & Tanning Materials (69)
6. Foodstuffs & Provisions (71)
7. Fuels (96)
8. Hardware (109)
9. Liquor, Intoxicating (122)
10. Machinery & Engines (135)
11. Manures (148)
12. Metals (150)
13. Minerals & Ores (152)
14. Nuts & Seeds (167)
15. Oil & Fats (176)
16. Paints (177)
17. Paper & Paperware (178)
18. Piece Goods & Textiles (226)
19. Railway Materials (228)
20. Tobacco (229)
21. Treasure (232)
22. Vehicles (234)
23. Wearing Apparel (236)
24. Bags (308) (after Sundries:- at 307)
25. Electric Apparatus (319)
26. Hides (All Kinds) (332)
27. Leather (All Kinds) (345)
28. Matches & Match Making Material (358-360) (label split)
29. All Other Sundries (373)
30. Total (382)

Now, we need to extract numbers between these labels.

We'll go through token stream from token 20 to 381, and assign numbers to current row until we have 12, then move to next row when we encounter next label.

But labels are not always exactly at token boundaries; some labels are multi-token (e.g., "Matches & Match Making", "Material", "H"). We'll need to detect labels by matching known label list.

Simpler: Since we have the token list with indices, we can manually slice.

I'll write a Python-like mental script but do it manually for each row.

Let's list tokens from 20 to 381 with their content and whether they are numeric.

I'll create a list of (index, token, is_numeric). But numeric detection: token contains digits and possibly commas, periods, spaces, $, G, |, %, :, etc. We'll consider a token numeric if it's primarily a number. But multi-number tokens need splitting.

Given the complexity, I'll instead use the fact that the total row is correct and work backwards? Not feasible.

Given the time constraints, I'll produce a Markdown table with the rows I can parse with high confidence (first few rows) and for the rest, I'll indicate the data is garbled and use ... for missing numbers. But the user likely expects a full reconstruction.

However, the instruction: "Your most important task is to fix the text flow while preserving the original paragraph structure." This is a table, not paragraphs. The output should be a Markdown table.

I think the best is to reconstruct the table as accurately as possible by cleaning the numbers and aligning columns.

I'll attempt to parse the entire token stream programmatically in my reasoning.

Let me write a more systematic parsing.

I'll read the token stream sequentially, maintaining a current row. When I encounter a token that matches a known row label (or a label pattern), I finalize the previous row (if any) and start a new row. For each row, I collect numeric tokens until I have 12 numbers or a new label appears.

But the token stream has labels that are not in my list (like "IFF", "H", "Material", "Sundries:-"). I'll need to handle them.

Let's define the known labels in order as they appear in the token stream (including multi-token labels). I'll scan the token stream for label-like tokens.

From the token list, the labels appear at indices: 19, 32, 45, 47, 69, 71, 96, 109, 122, 135, 148, 150, 152, 167, 176, 177, 178, 226, 228, 229, 232, 234, 236, 307, 308, 319, 332, 345, 358, 359, 360, 373, 382.

But 307 is "Sundries:-", 308 "Bags". 358 "Matches & Match Making", 359 "Material", 360 "H". So the label for row 28 is "Matches & Match Making Material" (tokens 358-360). Token 360 "H" might be a stray or part of label? Probably "H" is a misread of something else. I'll combine 358-359 as label.

Also, "Oil & Fats", "Paints", "Paper & Paperware" are three separate labels at 176,177,178.

Now, the numeric tokens are all others.

We