Sorry to bother you again and again.
I reduced the training text to about 450 lines, with like 30 ± in it. I 
used two fonts and iteration of 1000. But it looks like ± is still not 
picked up by the BEST OCR TEXT at all, it always recognizes ± as something 
else. What is happening here? Should I increase the number of ±? Or do I 
need to increase the number of fonts? I'm trying increasing iterations.

在 2019年6月18日星期二 UTC-4上午12:28:25,shree写道:
>
> If you increase the iterations then the plus type of training will not 
> give good result, i.e. the other letters will lose accuracy.
>
> You can try to reduce the training text size while still keeping all the 
> characters that you need as part of the training text, 
>
> On Tue, Jun 18, 2019 at 2:24 AM Jingjing Lin <joejo...@gmail.com 
> <javascript:>> wrote:
>
>> I was only using two different fonts and It only achieved lowest error 
>> rate of 11.271 after the training, does this mean I really need to increase 
>> the iterations?
>>
>> 在 2019年6月17日星期一 UTC-4下午2:16:31,shree写道:
>>>
>>> How big was your training text? How many iterations? Did the fonts you 
>>> use for training support the plus minus sign? 
>>>
>>> You can run training with -- debug-level of -1 so that you can see 
>>> whether the plus minus is being picked for training in the console messages.
>>>
>>> On Mon, 17 Jun 2019, 23:29 Jingjing Lin, <joejo...@gmail.com> wrote:
>>>
>>>> Thanks. It works. The new character I added was there.
>>>>
>>>> Do you have any idea why after fine tuning tesseract still couldn't 
>>>> recognize the new character I added? When I tried to add '±' to eng it 
>>>> works, but when I tried to add '±' to chi_sim, it couldn't work (explained 
>>>> below). Is there anything we need to pay attention to when fine tuning 
>>>> other langs rather than eng?
>>>>
>>>> I used 
>>>>
>>>> lstmeval --model ~/tesstutorial/trainplusminus/plusminus_checkpoint \
>>>>   --traineddata ~/tesstutorial/trainplusminus/chi_sim/chi_sim.traineddata \
>>>>   --eval_listfile ~/tesstutorial/evalplusminus/chi_sim.training_files.txt 
>>>> 2>&1 |
>>>>   grep ±
>>>>
>>>> to check and ± only shows up in Truth but not in OCR
>>>>
>>>>
>>>> 在 2019年6月17日星期一 UTC-4上午11:31:24,shree写道:
>>>>>
>>>>> combine_tessdata -u new.traineddata new.
>>>>>
>>>>> will unpack the traineddata file. check new.lstm-unicharset in it
>>>>>
>>>>> On Monday, June 17, 2019 at 8:20:24 PM UTC+5:30, Jingjing Lin wrote:
>>>>>>
>>>>>> I tried to fine tune the model and add a new character via training, 
>>>>>> but it seems it still couldn't recognize this new character using the 
>>>>>> new 
>>>>>> traineddata generated. To debug I want to check whether this new 
>>>>>> character 
>>>>>> is in the .unicharset in the new traineddata generated. Is there a way 
>>>>>> to 
>>>>>> do this?
>>>>>>
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>
>
> -- 
>
> ____________________________________________________________
> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>

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