Liverpool University Group 64 Year 2 project

Project Plan. 

Week 1:-  Preparation. Division of work
                                        = Bench
                                           Code
                                           Poster
                                           Report
                                           Blog
Week 2:- Update blog:
                             Pre-process
                             /
                       Code
                            \
                            Recognition
 Find data and theory of the project and also background information.

Week 3:-  Complete Preprocess of code
               Prepare for the recognition part
               Write: abstract, introduction "( Report )"
               Update blog. - Sustainability.

Week 4:-  General complete the code
               Report
               Update poster
               Poster

Week 5:- Complete Report
              Final revision of code
              Record in blog
              Poster and QR code

Week 6:- Final code test
              Polish Poster
              Report
              Prepare for bench inspection

Introduction                

The process of the recognition of the handwritten digits can be divided into three main parts that are pre-processing, feature extraction and recognition.

In the pre-processing stage, some necessary work should be done on the collected images to make the recognition perform successfully, which includes: the image geometric correction, noise removal, recovery, two values, word division, etc.

In the feature extraction stage, after processing the image, it has many features, it is impossible to use all of the identifying characteristics. Therefore, some effective features will be extracted and can be used in the method of the recognition of the digits.

In the recognition stage, the recognition will be performed by using the extracted features. The handwritten recognition methods can be divided into several categories, and neural network method is more popular research method , the basic principle is the use of neural network learning and memory function, let neural network learning a lot each mode category learning samples to remember the characteristics of each sample pattern category , then in identifying the sample to be identified , recalls the neural network model before starting to remember each category individually characterized and compared with samples of their characteristics , in order to determine the sample belongs model category . The advantage of this approach is a strong anti-jamming capability, allowing a larger sample changes, but it also depends on the selection of feature vectors.