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Updated Data Files in tessdata_fast (markdown)

Shreeshrii 2019-08-07 13:56:14 +05:30
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Data-Files-in-tessdata_fast.md

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## Traineddata Files for Version 4.00
## Traineddata Files for Version 4.00 +
We have three sets of .traineddata files for tesseract on GitHub in three separate repositories.
We have three sets of .traineddata files for `tesseract` versions 4.00 and above on GitHub in three separate repositories.
* [tessdata_fast](https://github.com/tesseract-ocr/tessdata_fast) (Sep 2017) best "value for money" in speed vs accuracy, Integer models
* [tessdata_best](https://github.com/tesseract-ocr/tessdata_best) (Sep 2017) best results on the eval data, slower, Float models, can be used as base for finetune training
* [tessdata](https://github.com/tesseract-ocr/tessdata) (Nov 2016) supports legacy tesseract engine also
* [tessdata_best](https://github.com/tesseract-ocr/tessdata_best) (Sep 2017) best results on Google's eval data, slower, Float models. These are the only models that can be used as base for finetune training
* [tessdata](https://github.com/tesseract-ocr/tessdata) (Nov 2016 and Sep 2017) These have legacy tesseract models from 2016. The LSTM models have been updated with Integer version of tessdata_best LSTM models. (Cube based legacy tesseract models for Hindi, Arabic etc. have been deleted).
When using the models in the **`tessdata_best`** and **`tessdata_fast`** repositories, only the new LSTM-based OCR engine is supported. The legacy tesseract engine is NOT supported with these files, so Tesseract's oem modes '0' and '2' won't work with them.
When using the traineddata files from the **`tessdata_best`** and **`tessdata_fast`** repositories, only the new LSTM-based OCR engine is supported. The legacy tesseract engine is NOT supported with these files, so Tesseract's oem modes '0' and '2' won't work with them.
## Information specific to tessdata_fast
First, fast is trained with a spec that produces a smaller net than best. As a result of smaller model, the prediction will be faster.
Then, the float->int conversion is done, which further reduces the size of the model and makes it even faster if your CPU supports AVX2.
First, `fast` is trained with a spec that produces a smaller net than `best`. As a result of smaller model, the prediction will be faster.
Then, the `float->int conversion` is done, which further reduces the size of the model and makes it even faster if your CPU supports AVX2.
### Usage