1. PROJECT PROPOSAL DIGITAL IMAGE PROCESSING TITLE:- Automatic Machine Written Document Reader Project Partners:- Manohar Kuse(Y08UC073) Sunil Prasad Jaiswal(Y08UC124)

2. AIM To build a system, which could read aloud the text in a book, by means of recognition of characters from the image of the pages of the book from a simple overhead web-cam. Our Approach  First we will acquire a page of text book by means of overhead web-cam. This will work as our input image.  Now we will convert the text in image to ASCII text.  This process will be carried by means of first separating individual characters from the image. Identifying them with some machine learning approach.  The ASCII test thus formed will be sent to program which can convert this ASCII text to sound.  SEGMENTATION OF CHARACTERS:  We plan to achieve this by First thresholding the acquired image, to obtain a binary mask of individual characters.  Then by means of CCA (connected component analysis) label individual characters and then normalize the size of each connected component.  CHARACTER-FEATURES:  One of the method suggested, was to use statistical zonal features. Which means, that an individual character was divided into a few zones. And statistical features like mean, variance, area, perimeter etc of each zones were the features.  These features are to be later learned with classifiers like – Neural networks, Bayer's classifier or Support Vector Machines (SVM).

3.  We are planning to divide the normalized character into 16 zones. For each zone we will evaluated mean, variance, area, perimeter. We are also planning to use global features like – compactness, area, perimeter and fraction of filled area.  Block diagram of our approach :- image Acquisition Segmentation of characters &size normalization Character feature extraction Identification Sound producing device

4. Challenges  CCA is not able to identify spaces. This will create problems in grouping characters as words. We plan to come-up with a new approach for segmentation of characters.  Since we are using zonal features, slight orientation of characters might cause inaccuracy for the classifier.  Requirement of large dataset, covering various font styles, and symbols. We have not yet found such a data-set on the internet and we plan to make one by ourselves, and get our friends for manual annotation.. Course of Progress We plan to have following deadlines:- 28 th Oct------------------Finalization of character features 2 nd Nov-------------------Building the data-set with annotations 4 th Nov-------------------Preliminary results on the data-set 10 th Nov-----------------Testing with real time data 12 th Nov-----------------Testing results 15 th Nov-----------------Demonstration of the system 16 th Nov------------------Final report