Year 2 Project

  Introduction The aim of this week was to carry out further tests on the handwriting recognition software program to make certain that it works as expected and any limitations are known and considered. This main objective was to have a deliverable product ready by the end of the week for assessment (during week 5's bench inspection). Project Progression Some of the results form further tests carried out during this week:  Test Result 1 (unsuccessful at fully recognising all the written characters) Test Result 2 (unsuccessful at f ully recognising all the written characters) As can be seen from the pictures above, further testing on the program revealed a limitation that hadn't been observed prior: some letters being completely ignored by the program and therefore not being recognised at all. After several attempts of testing different handwritings, it was found that writing characters straighter and each with the same size had a greater chance of being recognised by the progra

Introduction The focus of this week was to create a functional GUI that would assist in navigating through our software product easily and efficiently. With the addition of a GUI to the software program, the development of our handwriting recognition product would be complete (excluding some more testing to be carried out in week 4). Furthermore, a few hours were spent completing the sustainable development and ethics assignment for this project. Project Progression After learning how to create a GUI on python last week, a simple GUI was developed for our handwriting recognition program. Handwriting Recognition GUI Interface The picture above shows the window that is displayed for our GUI and highlights the operations that it can perform: allow images to be chosen from users computer library, apply handwriting recognition algorithm to selected images, show accuracy of handwriting recognition on said images, and lastly exit out of the GUI interface. The following pictures below show the

Introduction After finishing up learning about GUI's last week, all research aspects of the project were complete. This week focused more on the coding aspects of the project as most of the code for the handwriting recognition software had been written during this week. Additionally, a start was made towards the report writing. Project Progression The practice code that was used for image recognition of animals in week 0 was used as a basis to begin writing the code for the handwriting recognition software. Throughout the week, with continuous testing and improvements to the code, significant progress had been made towards developing the final software.  Initially, the first draft of the code had problems recognising many of the written characters, specifically the letter 'O'.  As can been seen from the examples above, the code had difficulty recognising the character 'O' with a reasonable level of accuracy. Furthermore, sometimes the testing of the code would give

Introduction After completing initial research into handwriting recognition systems and understanding how they work, plans were made to begin developing the final code on Python. Furthermore, the first academic advisor meeting regarding the project was held where ideas for how the project would be developed was discussed. Project Progression After researching into different types of handwriting recognition software , a word document summarising the useful information that was learnt was created. This document contains information such as definitions for key terminology used in a handwriting recognition software (e.g. neural networks, activation function, epoch etc..) as well as notes on training, validation and test sets that will be implemented into the program during its development. Below are a few pictures highlighting some of the information found in this document. In short, this document will be used by the team to quickly refer back to concepts they learnt during the research we

Introduction This project aims to create a software programme that can read handwritten text from images and convert them into typed text. The language used will be Python, using tensor flow keras and deep learning in order to create a software able to learn to improve its accuracy in recognising written words and numbers. The team members are: Alvin Chan (project leader), Mohamed Mohamed and Roshan Tripathi. Progress made thus far Before the new (2nd) semester had begun, several meetings were held to discuss how the project would be executed. The aim during this week was to allow the members to research and learn more about how to carry out the project, i.e. how exactly the handwriting recognition could be written into code, the difficulties associated with it and its reliability. Furthermore, the team spent time gaining more familiarity with the Python language during this time. Alvin set the pace expected of himself and his team from the get go with the meetings and works done betwe