It still couldn't work after I increased the number of ± to about 100. And 
the error rate after 2000 iterations is about 11. This is a pretty high 
error rate compare to what we have for adding a few characters to eng. With 
such high error rate, I would not be surprised that it could't recognize 
some special characters like ±. Is this it for chi_sim? Or can I increase 
iterations to make the error rate smaller? 
Thanks for your help.

在 2019年6月18日星期二 UTC-4上午10:32:37,shree写道:
>
>  increase the number of ± to about 100 
>
> On Tue, Jun 18, 2019 at 7:39 PM Jingjing Lin <[email protected] 
> <javascript:>> wrote:
>
>> 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 <[email protected]> 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, <[email protected]> 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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>>>>>> .
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>>>
>>>
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>>>
>>> ____________________________________________________________
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>>>
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>
>
> -- 
>
> ____________________________________________________________
> भजन - कीर्तन - आरती @ http://bhajans.ramparivar.com
>

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