Handwritten Digit Recognition Using Neural Network

Handwritten digit recognition is a familiar issue in the domain of machine learning (ML). For this research, the most analyzed datasets like the MNIST (Modified National Institute of Standards and Technology) dataset is utilized. This dataset contains 28×28 grayscale images of handwritten digits (0-9) and a general technique to handle this issue by using neural networks, specifically in Convolutional Neural Networks (CNNs). Guidance will be assured for you to understand the concepts of Handwritten digit recognition by providing brief explanation. We have helped scholars for past 18+ years to outshine in their academic growth we serve as a bridge to fulfill your dreams.

       The following are processing steps which we implement to construct a handwritten digit observation mechanism using neural network:

  1. Data Collection:
  • We employ the MNIST dataset that has 60,000 training images and 10,000 validation images in it.
  1. Pre-processing the Data:
  • Normalization: Dividing by 255 to transform pixel values from a range of 0-255 to 0-1 in our project.
  • Reshaping: When utilizing a CNN, we reshape input data from (28, 28) to (28, 28, 1) showing the grayscale images.
  • Label Encoding: Changing labels from integer format like 7 to one-hot encoded vectors like [0, 0, 0, 0, 0, 0, 0, 1, 0, 0,] is beneficial for us.
  1. Model Architecture:

     For this work we offer an easy CNN framework which looks like:

  1. Input Layer: 28 x 28 x 1
  2. Convolutional Layer: Has 32 filters, a kernel size of 3×3, and ReLU activation.
  3. Max-Pooling Layer: With pool size 2×2.
  4. Convolutional Layer: Has 64 filters, a kernel size of 3×3, and ReLU activation.
  5. Max-Pooling Layer: With pool size 2×2.
  6. Flatten layer: Flattening the 3D output to 1D.
  7. Fully Connected Layer (Dense Layer): Contains 128 neurons and ReLU activation.
  8. Output Layer: With 10 neurons (one for each digit) and to get possibilities, this layer use Softmax activation.
  9. Training:
  • Loss Function: Our system incorporates the categorical cross-entropy loss because it is a multi-class classification issue.
  • Optimizer: We involve general options like Adam and RMSprop.
  • Metrics: During training monitor accuracy of our model.
  • Batch Size & Epochs: Frequent options are a batch size of 32 or 64 and training for 10-30 epochs assist us, but these get differ in terms of experiment.
  1. Evaluation:
  • Check our system’s efficiency on the validation set with accuracy as the basic metric.
  • To interpret where the framework is creating troubles, we implement a confusion matrix.
  1. Refinement:
  • Data Augmentation: By applying conversions such as rotations, shifts, and zooms to the training data we raise the strength of our system.
  • Hyperparameter Tuning: Our model practice with various network frameworks, batch sizes, learning rates and others.
  • Regularization: When it is necessary to avoid overfitting we eliminate layers and L2 regularization methods.
  1. Deployment:
  • Once our framework is trained, we apply it in several applications, web services and tools where the users upload their handwritten digits to get real-time detections.


     Particularly in neural networks, we use CNNs for handwritten digit recognition and it is a certain achievement topic in the field of deep learning. It is attainable to make success above 99% accuracy by the MNIST in an improved framework.

Handwritten Digit Recognition using Neural Network Thesis Topics

Handwritten Digit Recognition Using Neural Network Thesis Topics

                       New thesis topics will be suggested as a good thesis topic should have statement of research problem which should be brief. You can gladly rely on us we take in personalized approach for your research needs by tailoring handwritten digit recognition thesis writing. By outcome of our work, you can come to know about us as we have accommodated more than 3000+ projects successfully and on time delivery.

The latest and current handwritten digit recognition thesis topics that we have assisted are shared below.

  1. Secure communication and implementation of handwritten digit recognition using deep neural network
  2. Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition
  3. A Novel Technique for Handwritten Digit Recognition Using Deep Learning
  4. Efficient Approach to Using CNN-Based Pre-trained Models in Bangla Handwritten Digit Recognition
  5. Handwritten digits recognition using transfer learning
  6. Enhanced handwritten digit recognition using optimally selected optimizer for an ANN
  7. Handwritten Digits Recognition Based on a Parallel Optoelectronic Time-Delay Reservoir Computing System
  8. Dzongkha Handwritten Digit Recognition using Machine Learning Techniques
  9. Comparative Analysis of Handwritten Digit Recognition Investigation Using Deep Learning Model
  10. Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder
  11. Handwritten Digit Recognition Using Machine Learning
  12. Artificial Intelligence Based Handwriting Digit Recognition (HDR) – A Technical Review
  13. Handwritten Digit Recognition using Deep Neural Networks
  14. Handwritten Character and Digit Recognition with Deep Convolutional Neural Networks: A Comparative Study
  15. Handwritten Digit Recognition Based on Convolutional Neural Network
  16. The Smart Handwritten Digits Recognition Using Machine Learning Algorithm
  17. Folded Handwritten Digit Recognition Based on WGAN-GP Model
  18. Recognition of Bengali Handwritten Digits Using Spiking Neural Network Architecture
  19. Digit Recognition of MNIST Handwritten Using Convolutional Neural Networks (CNN)
  20. A Comparative Study of Handwritten Digit Recognition Algorithms
  21. MNSIT Handwritten Digit Recognition Using Machine Learning Classification Algorithms
  22. Hybrid Approach for Handwritten Digit Recognition using Deep Learning and ESRGAN-based Image Super-Resolution
  23. HDSRNet: A Simple Segmentation-free Method for Unconstrained Handwritten Digit String Recognition
  24. Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection
  25. Design and optimization of an on-chip Artificial neural network on FPGA for recognizing handwritten digits
  26. KNN and the CNN for Handwritten Digit Recognition: A comparative study
  27. Performance Analysis of a Handwritten Digit Recognition Using CNN-WOA
  28. Hybrid Deep Learning Model for Handwritten Bangla and English Digit Recognition
  29. ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers
  30. Implementation and Optimization of LeNet-5 Model for Handwritten Digits Recognition on FPGAs using Brevitas and FINN
  31. Threshold-type memristor-based crossbar array design and its application in handwritten digit recognition
  32. A vast dataset for Kurdish handwritten digits and isolated characters recognition
  33. Handwritten Digit Recognition Using Encryption Methods
  34. Evaluation of Supervised Machine Learning Models for Handwritten Digit Recognition
  35. Two Decades of Bengali Handwritten Digit Recognition: A Survey
  36. MNIST Handwritten Digit Recognition using Machine Learning
  37. Handwritten Digit Recognition using Deep Learning Networks
  38. Ensemble machine learning model for classification of handwritten digit recognition
  39. Smart Bot for Handwritten Digit String Recognition
  40. Recognition of Handwritten Digit Using Neural Networks


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