Handwritten Character Recognition Using Neural Network

Handwritten character recognition, frequently known as Optical Character Recognition (OCG) for handwritten text, is a difficult issue due to the variability in the individual handwriting styles. Neural networks, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs) have been increasingly efficient in this field. Getting a strong thesis statement is a crucial task our professional thesis writers help you to go through all the odds by sharing novel ideas. Here we give a step-by-step guidance to execute handwritten character recognition by employing a Neural Network:

  1. Data Collection:
  • Dataset: For handwritten digit recognition, the Modified National Institute of Standards and Technology (MNIST) dataset is supported by us. For more prolonged characters, our model utilizes the EMNIST (Extended MNIST) datasets that involve letters as well.
  1. Data Pre-processing:
  • Normalization: For normalization, our project scales the pixel values from [0,255] to [0, 1] by dividing 255.0.
  • Reshaping: In data preprocessing, we require a reshape of 2828 pixel image into 28 1 (adding a channel dimension), when employing a CNN.
  • One-hot Encoding: For classification, our model uses one-hot encoding by labeling 0-9 for digits and more for characters.
  1. Model Building:
  • Using a CNN: For image data CNNs are general. Some of the basic architectures involves are:
  • Convolutional Layers (with activation, e.g., ReLU)
  • Pooling Layers like Max pooling
  • Fully connected layers like dense layers
  • Output layer with softmax activation, when it is 10 neurons for digits and if it is more we identify characters as well.
  • Using a RNN (like LSTM): LSTM is valuable for consecutive handwriting like cursive. From handwritten lines, the LSTMs can process sequences of image slices and identify consecutive characters.
  1. Model Training:
  • Loss Functions: For multi-class classification, we employ categorical cross entropy technique.
  • Optimizer: For good initial points, our model incorporates Adam, RMSprop, or SGD.
  • Batch Size and Epochs: Our research initiates with a small batch size like 32 and that improves as required. Till the validation accuracy plateaus, we train the number of epochs.
  • Validation Split: To monitor overfitting, employ a segment of the training data like 10% for validation during training.
  1. Evaluation:
  • Accuracy: By using a test set we estimate accuracy. For MNIST, the top efficiency frameworks perform over 99% accuracy.
  • Confusion Matrix: Our framework frequently confuses others, and helps to find the characters.
  1. Post-processing:
  • Thresholding: For utilizing confidence thresholding is helpful for us, by only accepting forecasting if the model is necessarily confident.
  • Dictionary Matching: To make sure that the identified sequences correspond with real words in the language otherwise we choose the corresponding word.
  1. Deployment:

            If the model is fulfilled for us, we deploy the framework for real-world handwritten character identification. By taking into account our model, we use the platforms like TensorFlow Lite or ONNX for on-device deployment.  


  1. Data Augmentation: To create more training samples, our model improves the variability in training data by applying random transformations like rotation, scaling and shifting.
  2. Complex Architectures: For identifying entire handwritten words or sentences, we look into most complicated architectures such as the mixture of CNNs and RNNs.
  3. Transfer Learning: Pretrained frameworks can be employed on relevant tasks as an initial point and fine tune them for handwritten recognition.
  4. Regularization: Our work employs dropout or L2 regularization to avoid overfitting.

            Recall that while for handwritten character recognition, neural networks are highly efficient, performing increased accuracy that needs a lot of fine-tuning and experimentation with the architecture, training process, and post-processing steps.

Handwritten Character Recognition Using Machine Learning

Handwritten Character Recognition Research Topics

The topics that we have mentioned below are few samples that we have worked previously, so get experts touch in your work we give scholars full support along with its methodology. You can get plagiarism free paper from our experts team as it is our major work ethics.

  1. Recognition of Tamil handwritten characters using Scrabble GAN
  2. Handwritten Character Recognition Using Machine Learning
  3. Handwritten Character Recognition Based on Adabelief Optimized Convolutional Neural Network
  4. Offline Handwriting Recognition of Thai Characters Using Multiple Deep Neural Networks
  5. Malayalam Handwritten Character Recognition using Transfer Learning and Fine Tuning of Deep Convolutional Neural Networks
  6. Handwritten Character and Digit Recognition with Deep Convolutional Neural Networks: A Comparative Study
  7. A Novel Approach to Recognize Handwritten Telugu Words Using Character Level CNN
  8. Optical Character Recognition for Handwritten Telugu Text
  9. Offline Handwritten Basic Telugu Optical Character Recognition (OCR) using Convolution Neural Networks (CNN)
  10. Handwritten Chinese Character Recognition Based on Morphology and Transfer Learning
  11. Recognizing Handwritten Offline Tamil Character using VAE-GAN & CNN
  12. Malayalam Handwritten Character Recognition Using Transfer Learning
  13. A lightweight off-line handwritten Chinese character recognition algorithm based on gradient feature and channel attention
  14. Fast and Robust Online Handwritten Chinese Character Recognition With Deep Spatial and Contextual Information Fusion Network
  15. Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
  16. Crow Search Freeman Chain Code (CS-FCC) Feature Extraction Algorithm for Handwritten Character Recognition
  17. A Kannada Handwritten Character Recognition System Exploiting Machine Learning Approach
  18. Handwritten Character Recognition Using Directed Acyclic Graph
  19. A Literature Survey on Handwritten Character Recognition
  20. An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition
  21. Handwritten Character Recognition of Telugu Characters
  22. Handwritten Character Recognition System using Deep Learning Models for Tamil Language
  23. Recognition of Handwritten Character using Recognition Model based on SVM
  24. Thai Handwritten Character Recognition Using Deep Convolutional Neural Network
  25. Zero-Shot Offline Handwritten Chinese Character Recognition with Graph Embedding
  26. An Ensemble approach of Pretrained CNN models for Recognition of Handwritten Characters in Bangla
  27. Comparative Study of Different Optical Character Recognition Models on Handwritten and Printed Medical Reports
  28. Leveraging deep feature learning for wearable sensors based handwritten character recognition
  29. Comparing filter and wrapper approaches for feature selection in handwritten character recognition
  30. Tamil Handwritten Character Recognition System using Statistical Algorithmic Approaches
  31. Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis
  32. Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification
  33. DMHC: Device-free multi-modal handwritten character recognition system with acoustic signal
  34. Handwritten devanagari manuscript characters recognition using capsnet
  35. A time efficient offline handwritten character recognition using convolutional extreme learning machine
  36. Multiple attentional aggregation network for handwritten Dongba character recognition
  37. Comparative study on the performance of the state-of-the-art CNN models for handwritten Bangla character recognition
  38. Handwritten Character Recognition from Image Using CNN
  39. Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN
  40. Word spotting and character recognition of handwritten Hindi scripts by Integral Histogram of Oriented Displacement (IHOD) descriptor

Why Work With Us ?

Senior Research Member Research Experience Journal
Research Ethics Business Ethics Valid
Explanations Paper Publication
9 Big Reasons to Select Us
Senior Research Member

Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Research Experience

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

Journal Member

We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

Book Publisher

PhDdirection.com is world’s largest book publishing platform that predominantly work subject-wise categories for scholars/students to assist their books writing and takes out into the University Library.

Research Ethics

Our researchers provide required research ethics such as Confidentiality & Privacy, Novelty (valuable research), Plagiarism-Free, and Timely Delivery. Our customers have freedom to examine their current specific research activities.

Business Ethics

Our organization take into consideration of customer satisfaction, online, offline support and professional works deliver since these are the actual inspiring business factors.

Valid References

Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.


Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Paper Publication

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

Related Pages

Our Benefits

Throughout Reference
Confidential Agreement
Research No Way Resale
Publication Guarantee
Customize Support
Fair Revisions
Business Professionalism

Domains & Tools

We generally use




Support 24/7, Call Us @ Any Time

Research Topics
Order Now