Hello Lorenzo,
We're fine tuning en.traineddata without modifications with charset 
restriction within [A-Z0-9]. We're using the default parameters and the 
model converges very fast.
We have #1376 images from Google image used to test the accuracy. The 
reported accuracy is min(detector, recognizer). These #1376 images can't be 
directly used with tesseract and requires a detector and preprocessor.

On Wednesday, May 29, 2019 at 10:08:53 AM UTC+2, Lorenzo Blz wrote:
>
> Hi Mamadou,
> this sounds very interesting. How did you do the training and accuracy 
> measurements? What parameters did you use for the model?
>
>
> Thanks, bye
>
> Lorenzo
>
> Il giorno lun 27 mag 2019 alle ore 07:38 Mamadou <[email protected] 
> <javascript:>> ha scritto:
>
>> Hello,
>>
>> We have open sourced (BSD license) MRZ/MRP (Machine-readable 
>> zone/passport) dataset and models for Tesseract v4.
>> The dataset contains more than #7 thousands images (.tif) with ground 
>> truth (.gt.txt) from Google image augmented with few synthetic data.
>> It's ready to be used to train with Tesseract v4.
>> If you're lazy and don't want to train the models by yourself then, try 
>> the ones under tessdata_best (float-model) or tessdata_fast (int-model) 
>> folders.
>>
>> Accuracy: 99.7%
>> Source code: https://github.com/DoubangoTelecom/tesseractMRZ
>>
>> Regards,
>>
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