Cnn for Combination of both digits and letters(small and capital) Hi I am new to machine learning can anyone suggest open dataset consists of both digits and letters(small,capital)
I want images consisisting of both digits and letters to train my cnn model and make the model recognize the real time images
Can anyone please suggest me that dataset link
Thanks in advance
As for me, the easiest path to what you are asking for is generating them yourself.
An Example
I usually grab some TTF fonts and put them into a directory so that I have variety for character identification.  Begin by importing dependencies and creating a generator function:
from PIL import Image, ImageDraw, ImageFont
from os import listdir

WIDTH       =   200
HEIGHT      =   100
MINX        =    20
MINY        =    20
MAXX        =    WIDTH-60
MAXY        =    HEIGHT-60
MINSIZE     =    24
MAXSIZE     =    48

fonts = [i for i in filter(lambda i:i[-3:]==&quot;ttf&quot;, listdir(&quot;../data/fonts&quot;))]

def generate_character_image():
    fonts = [i for i in filter(lambda i:i[-3:]==&quot;ttf&quot;, listdir(&quot;../data/fonts&quot;))]
    charset=list(&quot;0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ&quot;)
    while 1:
        img = Image.new('RGB', (WIDTH,HEIGHT), color = (255,255,255))
        font = ImageFont.truetype(f'../data/fonts/{fonts[randrange(len(fonts))]}', randrange(MINSIZE,MAXSIZE))
        canvas = ImageDraw.Draw(img)
        where = (randrange(MINX,MAXX), randrange(MINY,MAXY))
        character = charset[randrange(len(charset))]
        coords = canvas.textbbox(where, character, font)
        # The coordinates are the top left corner
        bounding_box = (coords[0], coords[1], coords[2]-coords[0], coords[3]-coords[1])
        #canvas.rectangle(coords, outline=0, width=1)
        canvas.text(where, character, font=font, fill=(0,0,0), anchor=&quot;la&quot;)
        yield (character, coords, img)

for i in range(5):
    character,coords,image = next(generate_captcha())
    print(f&quot;{character} is in Bounding Box: {coords}&quot;)
    display(image)
    print(image)

You can then use this to generate an infinite number of training samples pretty trivially.
Why This Might Be Good
Personally, I prefer to generate my datasets whenever possible.  It allows me to do the following:

Train without ever repeating a sample
The validation data changes every time (in fact, I'll often not bother with a validation step since every epoch uses different data)
Zero storage required

Hope this helps!