{"action":"closed","issue":{"url":"https://api.github.com/repos/tesseract-ocr/tessdata/issues/31","repository_url":"https://api.github.com/repos/tesseract-ocr/tessdata","labels_url":"https://api.github.com/repos/tesseract-ocr/tessdata/issues/31/labels{/name}","comments_url":"https://api.github.com/repos/tesseract-ocr/tessdata/issues/31/comments","events_url":"https://api.github.com/repos/tesseract-ocr/tessdata/issues/31/events","html_url":"https://github.com/tesseract-ocr/tessdata/issues/31","id":193103220,"number":31,"title":"4.0 alpha: traineddata size","user":{"login":"Shreeshrii","id":5095331,"avatar_url":"https://avatars.githubusercontent.com/u/5095331?v=3","gravatar_id":"","url":"https://api.github.com/users/Shreeshrii","html_url":"https://github.com/Shreeshrii","followers_url":"https://api.github.com/users/Shreeshrii/followers","following_url":"https://api.github.com/users/Shreeshrii/following{/other_user}","gists_url":"https://api.github.com/users/Shreeshrii/gists{/gist_id}","starred_url":"https://api.github.com/users/Shreeshrii/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Shreeshrii/subscriptions","organizations_url":"https://api.github.com/users/Shreeshrii/orgs","repos_url":"https://api.github.com/users/Shreeshrii/repos","events_url":"https://api.github.com/users/Shreeshrii/events{/privacy}","received_events_url":"https://api.github.com/users/Shreeshrii/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2016-12-02T12:16:50Z","updated_at":"2016-12-20T05:37:32Z","closed_at":"2016-12-20T05:37:32Z","body":"Refer discussion on https://github.com/tesseract-ocr/tesseract/issues/40\r\n\r\n\"For Hindi cube+tesseract has half the error rate of either on their own. ... if the new LSTM engine is better, then yes, cube is likely to get the chop for 4.00,\"\r\n\r\nHindi\"Tests complete. Decision made. Cube is going away in 4.00. ... Note in the above table that LSTM is faster than Tess 3.04 (without adding cube) in both wall time and CPU time! For wall time by a factor of 2.\" \r\n\r\nAlso see: https://github.com/tesseract-ocr/tesseract/issues/521\r\n\r\n\"Tesseract will currently refuse to initialize with just the lstm model in it, even if you use OEM_LSTM_ONLY as the OCR engine mode. For now, you can make it run using any existing traineddata file and adding your new lstm model and (optionally) the lstm dawgs.\"\r\n\r\nThe traineddata uploaded for 4.0 alpha for Devanagari script languages san, hin and probably also for mar and nep has files for both tess model and LSTM model. Since tests show that LSTM is better from both speed and accuracy points of view for Devanagari (complex scripts), I suggest that \r\n\r\n1. The various dawg files related to tess model be deleted from the traineddata file to reduce size.\r\n\r\n2. Since inttemp, pffmtable, shapetable and normproto files are currently required, I suggest replacing the inttemp, pffmtable, shapetable files with dummy files - I tested by replacing them with files from eng.traineddata which is smaller and it worked fine. Normproto is tested against the unicharset and gives error messages if changed and so can be left, till this requirement is removed.\r\n\r\n3. Add a config file choosing the LSTM engine to be used for the traineddata.\r\n\r\nBy doing the above, I was able to reduce the traineddata size for san from 40+ MB to 20+ MB.\r\n\r\n"}}
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