Toolkit for Searching Doujinshi Quotes
If you want to just using just message search engine, see https://github.com/project-violet/violet-message-search-local.
Currently, korean and english are only supported.
If you would like to support your language, please contact me.
This work takes about 3-6 months.
All methods are based on fuzzing searches. Even if only a part of the input word or sentence is included, even if there is a typo in the input word, it produces an appropriate search result.
/similar/(.*?): Search for quotes similar to the input word or sentence./contains/(.*?): Searches for quotes that contain the input word or sentence. Search results cannot be shorter than the search target quote./lcs/(.*?): Search for quotes using the longest common substring algorithm./wcontains/(<article id>.*?)/(.*?): Contains operation for quotes included in a specific article.
You must collect source data from ocr of e-hentai articles before message search.
Check this miner sub-directory.
In do-ocr.py, easyocr lib is used for ocr.
To fast processing, consider using cloud gpu instances.
Follow example is the source data of 2282012.
2282012
184
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[([[178, 60], [261, 60], [261, 113], [178, 113]], '07', 0.19612714730852396), ([[977, 189], [1019, 189], [1019, 203], [977, 203]], "WE'VE", 0.8541661858003754), ([[979, 199], [1017, 199], [1017, 215], [979, 215]], 'BEEN', 0.9992900141407915), ([[971, 213], [1025, 213], [1025, 227], [971, 227]], 'FRiENDS', 0.6850694977495023), ([[967, 225], [1029, 225], [1029, 239], [967, 239]], 'SINCE WE', 0.9925336773792324), ([[961, 239], [1031, 239], [1031, 253], [961, 253]], 'WERE Kids', 0.8792938921603629), ([[963, 251], [997, 251], [997, 265], [963, 265]], "DON'", 0.9794792532920837), ([[1005, 251], [1033, 251], [1033, 265], [1005, 265]], 'TRY', 0.9982992401031888), ([[969, 263], [1027, 263], [1027, 277], [969, 277]], 'AND HIdE', 0.2835430810847031), ([[959, 275], [1035, 275], [1035, 289], [959, 289]], 'IT FROM ME,', 0.5066334474027858), ([[971, 287], [1035, 287], [1035, 303], [971, 303]], 'CAN TELL', 0.8528749599116918), ([[955, 299], [1035, 299], [1035, 315], [955, 315]], 'WHEN SOME', 0.9835687002537459), ([[973, 313], [1025, 313], [1025, 327], [973, 327]], "THING'S", 0.9268682399152953), ([[891, 323], [949, 323], [949, 339], [891, 339]], "THERE'S", 0.9957560208993628), ([[967, 325], [1029, 325], [1029, 339], [967, 339]], 'OFF With', 0.5789156160073846), ([[884, 336], [955, 336], [955, 353], [884, 353]], 'ANYTHING', 0.9991192865084113), ([[981, 339], [1013, 339], [1013, 351], [981, 351]], 'YOU;', 0.3370426297187805), ([[385, 349], [429, 349], [429, 365], [385, 365]], 'WELL _', 0.8774575095001782), ([[885, 351], [955, 351], [955, 365], [885, 365]], 'YOU NEED', 0.9859541601002418), ([[373, 363], [449, 363], [449, 383], [373, 383]], 'THERE IS', 0.6973383110654519), ([[885, 363], [953, 363], [953, 379], [885, 379]], 'ME To DO', 0.3812021439452931), ([[883, 377], [955, 377], [955, 391], [883, 391]], 'JUST TELL', 0.9869176987826622), ([[367, 381], [449, 381], [449, 399], [367, 399]], 'ONE THING;', 0.5397469987851745), ([[891, 391], [915, 391], [915, 405], [891, 405]], 'ME_', 0.9308594717226423), ([[919, 391], [949, 391], [949, 405], [919, 405]], "I'LL", 0.7510882956522018), ([[355, 397], [465, 397], [465, 415], [355, 415]], 'CAN WE SLEEP', 0.9231365604593831), ([[899, 403], [939, 403], [939, 419], [899, 419]], 'DO iT:', 0.2946919119144343), ([[361, 413], [457, 413], [457, 433], [361, 433]], 'TOGETHER?', 0.9764299771718015), ([[371, 431], [449, 431], [449, 451], [371, 451]], 'JUST FOQ', 0.7670194236002761), ([[367, 443], [450, 443], [450, 469], [367, 469]], 'ToniGHT?', 0.3726763793386982), ([[959, 515], [1015, 515], [1015, 531], [959, 531]], "C'MERE_", 0.863110849121565), ([[236, 566], [286, 566], [286, 594], [236, 594]], 'Yay!', 0.6762074828147888), ([[1011, 821], [1037, 821], [1037, 833], [1011, 833]], 'Wiv', 0.45946781380606444), ([[909, 831], [961, 831], [961, 851], [909, 851]], 'Ehehe,', 0.23657130980058727), ([[534, 1201], [660, 1201], [660, 1246], [534, 1246]], 'JD~_', 0.06099150329828262), ([[73, 1417], [95, 1417], [95, 1435], [73, 1435]], '98', 0.9999998314126102), ([[540.120938695087, 26.055415691187505], [660.8188949124501, 56.418016380311585], [642.879061304913, 118.9445843088125], [522.1811050875499, 88.58198361968842]], '80', 0.4172123950359721), ([[577.1423139959815, 127.05274336744995], [692.8888891221156, 162.757741305848], [672.8576860040185, 221.94725663255005], [557.1111108778844, 187.242258694152]], '8) ', 0.40848723861419983), ([[908.1484039973755, 1060.0363267692926], [1022.9869720641811, 1124.4897167558447], [980.8515960026245, 1196.9636732307074], [866.0130279358189, 1132.5102832441553]], 'Rr', 0.08366168143714428)]
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...
Source Data is very dirty, so you need to clean it up.
hcache is simple to do this.
This tool filters out sentences with low accuracy and combines words that are close to each other to form a complete sentence that will fit into the speech balloon.
Follow example is the cache data of 2282012.
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{
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{
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...hcache-merger is tool of merging caches.
This tool merges all cache information and splits it into one or two or more files.
A mandatory parameter of hcache-merger is information on how many merge files are split.
Sufficient search performance is guaranteed when the service is divided into at least three instances.
Even when running on a CPU with more than 64 cores (r5.metal, c5.metal, m5.metal),
it shows lower search performance than running it by splitting it into three instances :(
fast-search is the core program for message searching.
This tool quickly searches for speech balloons through a fuzzy string search.
A minimum of 2 Core CPU and 16 GB of RAM are required. For AWS, r5.large is recommended. If there is not enough RAM, create a swap.
sudo su
mkdir -p /var/swap
dd if=/dev/zero of=/var/swap/swapfile bs=1M count=4000
mkswap -f /var/swap/swapfile
swapon /var/swap/swapfile
swapon -sThen, download the split merge file from each instance and start the service.
sudo apt update -y
sudo apt install libgomp1 -y
wget http://1.2.3.4/htext/merged-0.json
wget http://1.2.3.4/htext/fast-search/build/fscm
wget http://1.2.3.4/htext/forever.py
mv merged-0.json merged.json
chmod 777 fscm
nohup ./fscm 0.0.0.0 8864 <your-onw-token> &or using this.
import sys
import os
import os.path
import time
import shutil
from subprocess import Popen, PIPE
from datetime import datetime
while True:
process = Popen(['./fscm', '0.0.0.0', '8864', '<your-onw-token>'])
process.wait()