(result were rounded to the nearest whole number)

1. Tesseract-OCR out of the box, original image. 👎

   CER 100, WER 100, LDR 0

   As suspected from the score, this text extraction would not be useful

2. Tesseract-OCR out of the box with deskewing 😟 (gray scaling must be done for deskewing)

   CER 46, WER 147, LDR 56

   <aside> 💡 Note that since N is the number of words in the reference, the word error rate can be larger than 1.0, and thus, the word accuracy can be smaller than 0.0.

   </aside>

   With the deskewing and gray scaling, the results improve but the WER is not good

3. Tesseract-OCR out of the box with preprocessing (no deskewing) 😟

   CER 76, WER 100, LDR 27

   This was done to see the impact of deskew separate from the other preprocessing steps

4. Tesseract-OCR out of the box with deskewing and preprocessing 👍

   CER 19, WER 66, LDR 74

   We can clearly see by the rating results, that significantly improvement was made by the deskewing and preprocessing steps combination on the text extraction

5. Tesseract-OCR with deskewing, preprocessing, and using a preciously custom Trained model with StorySquad handwriting samples. the ssq.traineddata can be found here 😟https://github.com/dakotagporter/tesstrain

   CER 26, WER 85, LDR 71

   We can see the specific traineddata model did not generalize well in this instance. As our rating worsen by using the ssq.traineddata.

   How these results of Tesseract-OC compare with Google cloud vision text extraction?

6. Google.cloud.vision with no deskewing or preprocessing

   CER 8.73, WER 19.04, LDR 91

7. Google.cloud.vision with deskewing and gray scaling CER 0, WER 5, LDR 95