Handwritten Tamil Character Recognition using Convolutional Neural Network

1Mrs. P.Muthulakshmi

Assistant professor of Computer Applications,

The Standard Fireworks Rajaratnam College for Women, Sivakasi.

2Mrs.N.Rithiga Shree,

II MCA,

The Standard Fireworks Rajaratnam College for Women, Sivakasi,

Abstract

Handwritten character recognition plays a pivotal role in various applications, including document digitization, language preservation, and human-computer interaction. This paper presents a novel approach for recognizing handwritten Tamil characters, an essential component in preserving and promoting the rich Tamil language heritage. Leveraging the power of deep learning algorithms, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), our system achieves remarkable accuracy in recognizing handwritten Tamil characters.

The proposed system begins with the collection of a comprehensive dataset of handwritten Tamil characters, encompassing various writing styles and variations. Data augmentation techniques are employed to enhance the diversity of the dataset, thereby improving model generalization. Preprocessing steps, such as resizing, noise reduction, and normalization, are applied to ensure the data’s consistency and quality.

For feature extraction, a combination of CNNs and RNNs is employed. The CNNs are used to extract spatial features from the input images, while the RNNs capture the sequential information of the characters’ strokes. This dual approach enables the model to recognize characters accurately, even in cases of complex writing styles.

The system is trained using a deep learning framework, optimizing for loss minimization and accuracy maximization. Extensive experiments and cross-validation techniques are applied to fine-tune the model parameters and ensure robustness. Our results demonstrate high accuracy in recognizing handwritten Tamil characters, outperforming existing methods.

our proposed Handwritten Tamil Character Recognition System showcases the potential of deep learning algorithms in preserving and promoting the Tamil language. The system’s ability to accurately recognize handwritten characters opens doors to various applications, including digitization of historical documents, language education tools, and automation of data entry processes in Tamil-script-based applications. The presented approach serves as a valuable contribution to the field of Tamil language technology and paves the way for further advancements in handwriting recognition for other languages as well.

Keywords: Tamil Character Recognition, Handwritten Tamil CNN, RNN, Deep Learning.

Introduction:

 In an era marked by the exponential growth of digital data and the ever-increasing influence of machine learning and artificial intelligence, the preservation and utilization of linguistic diversity remain paramount. The Tamil language, with its rich cultural heritage and historical significance, poses a unique challenge in the context of modern technological applications. The complexities of handwritten character recognition are further heightened in the case of Tamil script, characterized by intricate strokes and diverse writing styles. This research endeavors to address this challenge through the development and implementation of a sophisticated Handwritten Tamil Character Recognition System. Leveraging advanced techniques in deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the proposed system seeks to surpass existing methodologies by enhancing accuracy and addressing the intricacies of Tamil handwriting.

In the context of our research, the identified gaps in existing methodologies, particularly in addressing the challenges unique to Tamil handwriting, underscore the necessity for a sophisticated Handwritten Tamil Character Recognition System. The hybrid CNN-RNN approach, combined with data augmentation techniques and advanced preprocessing steps, contributes to the evolving landscape of character recognition, promising enhanced accuracy and adaptability for handwritten Tamil characters. Our research builds upon and extends these foundational works, providing a novel and effective solution to the intricacies of Tamil script recognition.

Methodology

  1. Dataset Creation:

Comprehensive Collection: Gather a diverse dataset of handwritten Tamil characters, encompassing various writing styles and variations. This dataset should be representative of the natural diversity found in handwritten Tamil script.

Annotation: Manually annotate the dataset to ensure accurate labeling of each character, facilitating supervised learning during the training phase.

Data Augmentation:

Variation Injection: Apply data augmentation techniques to introduce variations in the dataset, such as rotations, scaling, and translations. This step aims to enhance the model’s ability to generalize across different writing styles and orientations.

Augmented Dataset Creation: Generate an augmented dataset by applying these variations to the original dataset, thereby expanding the diversity of the training set.

  • Preprocessing:

Resizing: Standardize the size of the input images to ensure uniformity throughout the dataset. Resizing reduces computational complexity and facilitates consistent feature extraction.

Noise Reduction: Implement noise reduction techniques to enhance the clarity of handwritten characters. This step helps mitigate the impact of irrelevant information on the model’s training.

Normalization: Normalize pixel values to a standardized range, typically between 0 and 1, ensuring uniformity in data representation and aiding in convergence during training.

  • Hybrid CNN-RNN Feature Extraction:

CNN for Spatial Features: Utilize Convolutional Neural Networks (CNNs) to extract spatial features from the preprocessed images. CNN layers, including convolutional and pooling layers, capture hierarchical spatial patterns in the characters.

RNN for Sequential Information: Employ Recurrent Neural Networks (RNNs) to capture the sequential information of the characters’ strokes. The sequential nature of RNNs is well-suited for recognizing the nuanced structure of handwritten Tamil characters.

Feature Fusion: Combine the output features from the CNN and RNN layers to create a hybrid feature representation. These fusion captures both spatial and sequential characteristics, enabling the model to recognize characters accurately, even in cases of complex writing styles.

  • Model Training:

Deep Learning Framework: Train the recognition system using a deep learning framework, such as TensorFlow or PyTorch, optimizing for loss minimization and accuracy maximization.

Supervised Learning: Employ a supervised learning approach, where the model learns from the annotated dataset to associate input features with corresponding character labels.

Parameter Tuning: Fine-tune model parameters through extensive experiments and cross-validation techniques to ensure robustness and optimize performance.

Validation and Testing:

  1. Validation Set: Separate a portion of the dataset for validation to monitor the model’s performance during training and prevent overfitting.

ii)         Testing Set: Evaluate the trained model on a separate testing set, not seen during training, to assess its generalization capabilities and performance on unseen data.

  • Performance Evaluation:

Accuracy Metrics: Assess the system’s performance using standard metrics such as accuracy, precision, recall, and F1 score.

Comparison: Compare the results with existing methodologies and benchmarks to demonstrate the superiority of the proposed Handwritten Tamil Character Recognition System.

By meticulously following this methodology, we aim to develop a robust and accurate system capable of recognizing handwritten Tamil characters, contributing to the broader field of character recognition and language preservation. The hybrid CNN-RNN approach, coupled with comprehensive dataset augmentation and preprocessing, forms the foundation for our innovative solution to the challenges posed by Tamil script recognition.

Model Training:

  1. Deep Learning Framework:

TensorFlow Implementation: The proposed Handwritten Tamil Character Recognition System is implemented using the TensorFlow deep learning framework. TensorFlow provides a versatile platform for constructing and training neural networks, and its extensive community support facilitates efficient troubleshooting and optimization.

  • Architecture Selection:

Hybrid CNN-RNN Architecture: The architecture of the recognition system incorporates a hybrid design, leveraging both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This dual-model approach is chosen for its ability to capture both spatial features and sequential information inherent in handwritten Tamil characters, thus enhancing the system’s recognition accuracy.

  • Optimization Techniques:

Loss Function: The system is optimized for loss minimization using categorical cross-entropy as the loss function. Categorical cross-entropy is suitable for multi-class classification tasks and aligns with the objective of minimizing the dissimilarity between predicted and true character labels.

Optimizer: The Adam optimizer is employed for gradient descent optimization. Adam combines the advantages of both Adagrad and RMSprop, providing adaptive learning rates and momentum, leading to faster convergence and improved model performance.

  • Training Hyperparameters:

Learning Rate: A moderate learning rate is chosen to balance rapid convergence without overshooting the optimal parameter values. The learning rate is fine-tuned through experimentation to strike the right balance between convergence speed and stability.

Batch Size: Batch size is optimized to balance computational efficiency and memory usage. A suitable batch size ensures that the model generalizes well while efficiently utilizing available resources during training.

Epochs: The number of training epochs is determined through experimentation and cross-validation, ensuring that the model converges adequately without overfitting or underfitting to the training data.

  • Rationale Behind Model Parameter Choices:

Balancing Model Complexity: The choice of regularization techniques and hyperparameters aims to strike a balance between model complexity and generalization. Regularization prevents overfitting, ensuring that the model captures essential patterns without memorizing noise from the training data.

Adaptability: The adaptive learning rate provided by the Adam optimizer enhances the model’s adaptability to varying gradients during training, promoting faster convergence and robust optimization.

  • Experimentation and Cross-Validation: The selection of hyperparameters is informed by extensive experimentation and cross-validation, ensuring that the model’s performance is not overly reliant on specific configurations and demonstrating its robustness across different datasets.

Through careful consideration of these elements, the model training process is designed to yield a Handwritten Tamil Character Recognition System that is not only accurate and efficient but also resilient to the challenges posed by diverse writing styles and variations in Tamil script.

 Experimental Setup:

  1. Datasets:

Comprehensive Handwritten Tamil Dataset: The primary dataset for training and evaluation is a comprehensive collection of handwritten Tamil characters, carefully curated to encompass diverse writing styles, variations, and orientations. This dataset is manually annotated to ensure accurate labeling for supervised learning.

Validation and Testing Sets: The dataset is partitioned into training, validation, and testing sets. The validation set is utilized during training to monitor the model’s performance and prevent overfitting, while the testing set remains unseen during training for unbiased evaluation.

  • Data Augmentation:

Variation Injection: Data augmentation techniques, including rotations, scaling, and translations, are applied to the training set to introduce variations and enrich the dataset. This augmentation enhances the model’s ability to generalize across different writing styles and orientations.

  • Preprocessing:

Resizing: Standardize the size of input images to a consistent format to facilitate uniform feature extraction.

Noise Reduction: Apply noise reduction techniques to enhance the clarity of handwritten characters, improving the model’s ability to discern relevant information.

Normalization: Normalize pixel values to a standardized range (typically between 0 and 1) to ensure uniform data representation and aid convergence during training.

  • Hybrid CNN-RNN Model Configuration:

Architecture: The hybrid CNN-RNN model architecture is configured with a specific number of convolutional and recurrent layers. The choice of layers is determined through experimentation, balancing model complexity and computational efficiency.

Hyperparameters: The learning rate, batch size, and other hyperparameters are fine-tuned through experimentation to optimize model performance and training efficiency.

  • Training Procedure:

TensorFlow Framework: The model is trained using the TensorFlow deep learning framework, implementing the Adam optimizer for gradient descent and categorical cross-entropy as the loss function for multi-class classification.

Early Stopping: Monitoring of the validation loss enables early stopping, preventing overfitting and ensuring the model’s generalization to unseen data.

  • Cross-Validation:

K-Fold Cross-Validation: To assess the robustness of the Handwritten Tamil Character Recognition System, K-fold cross-validation is employed. The dataset is partitioned into K subsets, and the model is trained and validated K times, each time using a different subset for validation and the remaining data for training.

Performance Metrics: Accuracy, precision, recall, and F1 score are computed for each fold to evaluate the model’s consistency and effectiveness across different data partitions.

  • Experiments:

Comparative Experiments: The proposed system is compared against existing methodologies and benchmarks for Handwritten Character Recognition, demonstrating its superiority and advancements in recognizing handwritten Tamil characters.

Parameter Sensitivity Analysis: Experiments are conducted to analyze the sensitivity of the model to changes in key parameters, ensuring that the chosen configurations are robust.

  • Hardware and Software Environment:

Hardware: Experiments are conducted on a machine with suitable hardware specifications, including GPUs, to expedite training times.

Software: TensorFlow and associated libraries are utilized for model implementation, and experiments are carried out in a Python programming environment.

Results and Discussion:

  1. Performance Metrics:

Accuracy: The proposed system achieves a high accuracy rate, demonstrating its effectiveness in recognizing handwritten Tamil characters across diverse writing styles and variations.

Precision, Recall, and F1 Score: Detailed metrics such as precision, recall, and F1 score provide insights into the system’s ability to correctly identify and classify characters, particularly in challenging scenarios.

  1. Comparison with Existing Methodologies:

Outperformance: Comparative experiments against existing methodologies and benchmarks consistently show that the proposed Handwritten Tamil Character Recognition System outperforms its counterparts. The hybrid CNN-RNN approach, coupled with data augmentation and preprocessing techniques, contributes to the superior recognition accuracy.

  • Cross-Validation Results:

Consistency: K-fold cross-validation results indicate the consistency of the model across different data partitions. The system demonstrates robust performance, showcasing its ability to generalize effectively to diverse handwriting styles.

  • Parameter Sensitivity Analysis:

Robustness: Experiments exploring parameter sensitivity reveal the robustness of the model to variations in key parameters. This analysis ensures that the chosen configurations are resilient and maintain optimal performance across different setups.

  • Recognition of Complex Writing Styles:

Dual-Model Approach: The hybrid CNN-RNN feature extraction approach proves effective in recognizing characters even in cases of complex writing styles. The combination of spatial features extracted by CNNs and sequential information captured by RNNs contributes to the model’s adaptability.

  • Future Directions:

Fine-Tuning and Optimization: Further refinement of model parameters and optimization techniques may lead to incremental improvements in recognition accuracy.

Extension to Other Languages: The hybrid CNN-RNN architecture and methodologies can be adapted for recognizing characters in other languages, contributing to the broader field of character recognition.

Top of Form

 Applications:

  1. Historical Document Digitization:

Preservation of Cultural Heritage: The accurate recognition of handwritten Tamil characters facilitates the digitization of historical documents, manuscripts, and archives. This application ensures the preservation of cultural heritage by making valuable historical content accessible in a digital format.

Enhanced Accessibility: Digitized historical documents become more accessible to researchers, scholars, and the general public, fostering a deeper understanding of Tamil literature, history, and traditions.

  • Language Education Tools:

Interactive Learning Platforms: The Handwritten Tamil Character Recognition System can be integrated into language education tools and platforms. Learners can use the system to practice writing Tamil characters, receive real-time feedback on their handwriting, and enhance their language proficiency.

Automated Grading: Language educators can leverage the system for automated grading of handwritten assignments, providing timely and objective feedback to students.

  • Data Entry Automation:

Efficient Data Processing: In applications requiring Tamil script-based data entry, such as forms, surveys, or databases, the recognition system contributes to automation. Handwritten inputs can be swiftly and accurately transcribed into digital formats, reducing manual data entry efforts.

Increased Productivity: Businesses and organizations dealing with Tamil-script data can streamline their data entry processes, leading to increased efficiency and productivity. This is particularly valuable in sectors like administration, research, and customer service.

  • Assistive Technologies:

Support for Individuals with Limited Writing Abilities: The recognition system can be integrated into assistive technologies to aid individuals with limited writing abilities. It provides a means for them to input text through handwriting, opening up communication and educational opportunities.

Inclusive Design: By incorporating the system into assistive devices, applications, or software, developers contribute to inclusive design practices, ensuring accessibility for a diverse range of users.

  • Archival Systems for Tamil Literature:

Digital Libraries: The system can be utilized to create digital archives of Tamil literature, including handwritten manuscripts and rare texts. This application contributes to the preservation and dissemination of Tamil literary works, making them available for future generations.

Searchable Databases: The digitized content can be transformed into searchable databases, allowing researchers and enthusiasts to explore and analyze Tamil literature with greater ease and efficiency.

  • Research in Linguistics and Language Technology:

Corpus Building: The system aids in building large corpora of handwritten Tamil characters, contributing to linguistic research and language technology development.

Advanced Language Processing: The availability of accurate handwritten data supports advancements in natural language processing (NLP) for Tamil, fostering the development of language models and applications.

  • Customized Font Design:

Font Recognition and Generation: The system can be extended to recognize and analyze handwritten Tamil characters for custom font design. This application is valuable in the creation of unique and culturally relevant fonts based on diverse writing styles.

The proposed Handwritten Tamil Character Recognition System, with its versatility and accuracy, opens doors to a range of applications that extend beyond traditional character recognition.

Conclusion: The development and implementation of the Handwritten Tamil Character Recognition System represent a significant stride in the intersection of deep learning, language technology, and cultural preservation. It stands as a testament to the potential of deep learning algorithms in preserving and promoting linguistic diversity. Its accuracy, adaptability, and range of applications position it as a valuable tool for various domains, from education to cultural preservation. As technology continues to play a pivotal role in language advancement, the system’s contributions pave the way for further innovations in handwriting recognition and language technology not only for Tamil but also for diverse languages globally.

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