Abstract
Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduction of Deep learning has changed Optical Character Recognition by using models like Recurrent Neural Networks,convolutional neural network .In this paper i am gonna compare the different models like CNN model and CRNN model with current State of art model Transformer based Optical Character Recognition KeyWords :Transformers ,Convolution Recurrent Neural Network, Handwritten ,Optical Character Recognition
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