The dataset I am considering for this project is

The main objective of this project is detecting
and identifying house-number signs from street view images. The dataset I am
considering for this project is street view house numbers dataset taken from
5 has similarities with MNIST dataset. The SVHN dataset has more than 600,000
labeled characters and the images are in .png format. After extract the dataset
I resize all images in 32×32 pixels with three color channels. There are 10
classes, 1 for each digit. Digit ‘1’ is label as 1, ‘9’ is label as 9 and ‘0’
is label as 10. 5 The dataset is divided into three subgroups: train set,
test set, and extra set. The extra set is the largest subset contains almost
531,131 images. Correspondingly, train dataset has 73,252 and test data set has
26,032 images.Figure 3 is an example of the
original, variable-resolution, colored house-number images where each digit is marked
by bounding boxes. Bounding box information is stored in digitStruct.mat file, instead of
drawn directly on the images in the dataset. digitStruct.mat file contains a
struct called digitStruct with the same length of original images. Each element
in digitStruct has the following fields: “name” which is a string containing
the filename of the corresponding image. “Bbox” is a struct array that contains
the position, size, and label of each digit bounding box in the image. For
example, digitStruct(300). Bbox (2). height means the height of the 2nd digit
bounding box in the 300th image. 5 This is very clear from Figure 3 that in SVHN dataset
maximum house numbers signs are printed signs and they are easy to read. 2
Because there is a large variation in font, size, and colors it makes the
detection very difficult. The variation of resolution is also large here.
(Median: 28 pixels. Max: 403 pixels. Min: 9 pixels). 2 The graph below
indicates that there is the large variation in character heights as measured by
the height of the bounding box in original street view dataset. That means the
size of all characters in the dataset, their placement, and character
resolution is not evenly distributed across the dataset. Due to data are not
uniformly distributed it is difficult to make correct house number detection