Handwritten Digit Recognition: Challenges, Solutions, and Feature Analysis for Neural Networks

Activity submission

Learning outcomes:

LO3: Investigate different use cases of deep neural networks for making predictions and generating new content.

LO4: Assess the mechanics of machine learning algorithms, such as deep neural networks.

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Instructions

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IMPORTANT NOTICE: Please ensure that you have checked your programme calendar for the due date for this assignment.

Guidelines

1. There are 7 pages and 3 questions in this assignment.
2. Make sure that you have carefully read and fully understood the questions before answering them. Answer the questions fully but concisely and as directly as possible. Follow all specific instructions for individual questions (e.g. “list”, “in point form”).

1. The assignment must be your own work only. Do not copy any text from the notes, readings, or other sources without proper attribution. Where you do use material from sources other than your own, make sure that you (1), acknowledge these sources appropriately in the text, and (2), provide a list of all references used. (For more information on referencing please also refer to this guidance ).

1. We encourage you to use AI tools to IMPROVE your answer, but please be aware that answers that evidently are solely generated by AI will lead to failing your assignment.

Mark allocation

Each question receives a mark allocation. However, you will only receive a final percentage mark and will not be given individual marks for each question. The mark allocation is there to show you the weighting and length of each question.

Think about how easy it is for the human brain to recognise digits. The recognition of digits and handwriting in general, irrespective of the way different individuals write, comes naturally to the human brain. However, the same cannot be said for machines. When scientists want to train an algorithm to recognise written text, it is a complicated process. There are a lot of nuances, features, and technical aspects that must be taken into consideration for the algorithm to ultimately identify digits accurately, irrespective of who has written it.

Question 1

Consider a sample of your own handwriting and identify the main challenges associated with the automatic identification of handwritten digits that an AI system would have to recognise what you have written.

Question 2

You are tasked with training a neural network to automatically identify handwritten digits. Recommend at least two ways to address the challenges you have identified above, in order to improve the effective functioning of this neural network.

Question 3

Consider the following digits and how they are written by different individuals: 1, 2, 3, 4, 5, 6, 7, 8, 9, 0. Assess at least five different features that the neural network will need to learn in order to correctly identify these different digits, specifically the way they are written, and the lines and shapes associated with handwriting. For example, when an algorithm is trained to recognise the number 1 in the UK, it will be trained to recognise a simple vertical line, and then use this learnt feature to predict the digit it sees. 

Assessment Requirements 

The assignment focuses on applying knowledge of deep neural networks (DNNs) to the recognition of handwritten digits, emphasizing practical understanding and critical reflection on AI and machine learning processes. The key requirements include:

1. Understanding Challenges in Handwriting Recognition (LO3 & LO4)

   • Identify difficulties AI faces in recognizing digits from personal handwriting samples.

   • Consider variability, legibility, orientation, and distortions in handwriting.

2. Proposing Solutions to Improve Neural Network Accuracy (LO3 & LO4)

   • Recommend strategies for training DNNs to overcome challenges.

   • Examples: data augmentation, normalization, feature extraction.

3. Feature Assessment for Digit Recognition (LO3 & LO4)

   • Analyse at least five features critical for accurate recognition of digits (0–9).

   • Features include lines, curves, loops, angles, and stroke continuity.

4. Plagiarism and Academic Integrity

   • Answers must be original, properly referenced if external sources are used.

   • Use AI tools only to improve clarity, not to generate answers entirely.

Deliverable format: Microsoft Word, first-person narrative where appropriate, concise and fully answered responses.

Approach by Academic Mentor

The Academic mentor guided the student systematically to ensure all learning objectives were met:

1. Question 1 – Identifying Challenges

   • Step 1: Collect a personal handwriting sample.

   • Step 2: Reflect on variations in style, size, slant, and spacing.

   • Step 3: Discuss AI-specific difficulties, including inconsistencies, overlapping strokes, or ambiguous shapes.

   • Step 4: Highlight examples where AI may misclassify digits due to these challenges.

2. Question 2 – Improving Neural Network Functioning

   • Step 1: Review neural network architecture suitable for image recognition (e.g., Convolutional Neural Networks).

   • Step 2: Recommend practical solutions:

     • Data augmentation (rotation, scaling, noise addition) to increase training robustness.

     • Preprocessing techniques like normalization and binarization for better input standardization.

   • Step 3: Explain why these solutions address the challenges identified in Question 1.

3. Question 3 – Feature Assessment for Digits

   • Step 1: Identify critical features for digit recognition:

     • Straight vs. curved lines, closed loops, stroke intersections, line continuity, angles.

   • Step 2: Analyse how the network learns these features through convolutional filters.

   • Step 3: Provide examples of feature recognition differences for various handwriting styles.

Outcome and Learning Objectives Covered

• Outcome: The student produced a detailed submission demonstrating understanding of the mechanics of deep neural networks, applied critical reflection to personal handwriting, and recommended actionable strategies to improve AI performance.

• Learning Objectives Achieved:

  • LO3: Explored use cases of DNNs for handwriting prediction.

  • LO4: Assessed the mechanics of machine learning algorithms and their feature-learning capabilities.

• Additional Skills Developed:

  • Analytical reasoning in AI context

  • Practical application of machine learning theory

  • Clear technical communication and structured academic writing

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