Our model consists of five layers i.e. two convolution-pooling layers with 20 and 40 feature maps respectively and two-by-two max-pooling and one fully-connected hidden layer with 100 neurons.
Processed handwritten images to resemble the images of training-set.
Our model takes images of handwritten text as input and predicts the output.
If input image has many lines, we have developed a segmentation algorithm which clips out every character and then forwards it to model.
We have worked on a program that can recognize handwritten text which contains english alphabets and digits . Just input the image and you will the get the text written on it.
We have used neural network which learns the parameters so that it can learn the scriblings of the alphanumerics. The learning part is done using a character dataset. When we give a input , the program crops out each character in the order they are written and fed to the network for recognition. And there we have the text.
We have worked on a neural network which is trained by a character dataset to recognize handwritten digits and alphabets within accuracy of 92%.
Worked on image processing by using scaling, normalization and translating the image so that it resembles MNIST trainset images.
Improved our model by using Chars74k dataset and by adding two convolution-pooling layers and one fully-connected hidden layer with 100 neurons which increased accuracy over 90%.
Developed a function for segmenting the image into individual characters, applying above methods to get the image recognized.
After segmentation, the characters get recognized including SPACES and then furthur words. After the words are recognized, they are corrected if some letters are wrongly recognized using a dictionary spell-corrector. Given a paragraph of multiple lines, finally it can be recognized.
To make an interface to ease the communication between user and the program. To recognize CAPTCHA.