It gave "|" as text.

When resized to 50*50, text is "N\". You should check whether font used in
the image, is a part of fonts on which English language was trained.

Thanks

On Sat, Oct 27, 2018 at 3:46 PM <vinaybabu2...@gmail.com> wrote:

> Can you try this new attached image for Alphabet "M" ?
>
> On Saturday, October 27, 2018 at 12:22:45 PM UTC+5:30, vinayb...@gmail.com
> wrote:
>>
>> I am using Pytesseract to recognise an image for number 5 and I'm stunned
>> that even after applying various filters like GlaussianBlur and Threshold
>> and applying dilation and erosion to remove the noise it still not able to
>> identify the image.
>>
>>
>> I am using Eng Trained data by default. Not sure where I am going wrong.
>> Do I need to include any other training file here?
>>
>>
>> Filters Tried:
>>
>>
>>           1: cv2.threshold(cv2.GaussianBlur(img, (9, 9), 0), 0, 255,
>> cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
>>
>>             2: cv2.threshold(cv2.GaussianBlur(img, (7, 7), 0), 0, 255,
>> cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
>>
>>             3: cv2.threshold(cv2.GaussianBlur(img, (5, 5), 0), 0, 255,
>> cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
>>
>>             4: cv2.threshold(cv2.medianBlur(img, 5), 0, 255,
>> cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
>>
>>             5: cv2.threshold(cv2.medianBlur(img, 3), 0, 255,
>> cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
>>
>>             6: cv2.adaptiveThreshold(cv2.GaussianBlur(img, (5, 5), 0),
>> 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2),
>>
>>             7: cv2.adaptiveThreshold(cv2.medianBlur(img, 3), 255,
>> cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2),
>>
>>
>>
>> *Training Data:*
>>
>> eng.traineddata
>>
>>
>> *Original Image: See Attached*
>>
>>
>>
>>
>>
>> --
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