Abstract:
Handwritten text detection from a natural image has a large set of difficulties. A systematic
approach that can automatically recognise text from handwriting, printed books, road
signs and also classifies text and nontext blocks from natural image has many significant
applications. For instance, visual assistance for visually impaired people, image understanding,
classification of text in image, implementing autonomous navigation system. Recent development
of deep learning approach has strong capabilities to extract high level feature from a kernel(patch)
of an Image. In this thesis we will demonstrate an alternate approach that integrates a multilayer
convolutional neural network (CNN) with supervised feature learning .This approach allows a
higher recall rate for the text in an image and thus increases the overall performances of the
system. And we have used these methodologies to create a learning model using synthetic and
real-world data that is capable to process bangla and english handwritten and scene text in
natural image.
