Optimized Malayalam Handwritten Character Recognition Model Using a Novel DSC and Stacked Bi-LSTM with Data Augmentation
Unlocking 99.75% Accuracy for Complex Malayalam Scripts
Leveraging Depthwise Separable Convolution (DSC) and stacked Bi-LSTM, this novel model achieves superior accuracy and efficiency in recognizing complex Malayalam handwritten characters, significantly reducing computational cost and overcoming misclassification challenges.
99.75%
Peak MHCR Accuracy for Complex Malayalam Scripts
6x
Parameter Size Reduction with DSC
90 classes
Malayalam Characters Recognized
162,000 samples
Augmented Dataset Size
1.4%
Accuracy Boost from Data Augmentation
Deep Analysis & Enterprise Applications
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Optimized DSC-Bi-LSTM Design
The proposed MHCR model combines Depthwise Separable Convolution (DSC) for efficient feature extraction and stacked Bidirectional Long Short-Term Memory (Bi-LSTM) for robust classification. DSC significantly reduces model size and computational cost by decoupling spatial and channel correlations. Bi-LSTM effectively handles high interclass similarities and distortions common in Malayalam script by capturing contextual information from both forward and backward sequences. This hybrid approach delivers superior performance and addresses key challenges in HCR.
Strategic Data Augmentation
To enhance model robustness and prevent overfitting, the system utilizes various image affine transformations: translation, rotation, scaling, and elastic deformation. Translation proved most effective, boosting accuracy to 99.51% on augmented data. The self-generated dataset was expanded to 162,000 grayscale images, ensuring balanced representation across 90 character classes. This strategic augmentation is crucial for achieving high accuracy with limited initial handwritten samples.
Comprehensive Preprocessing Workflow
A meticulous preprocessing pipeline ensures data consistency and feature enhancement. Key steps include skew detection and correction, novel adaptive segmentation (line and character isolation using Connected Component Labeling), smoothing with mean filters, image size normalization (to 28x28 pixels via bicubic interpolation), binarization using Otsu's method, and morphological operations like thinning, erosion, and dilation. This prepares the diverse Malayalam character samples for optimal feature extraction.
Benchmarking and Error Analysis
The DSC-Bi-LSTM model achieved a remarkable 99.75% accuracy on the self-generated augmented Malayalam dataset (Dataset-II) and 98.11% on the MNIST dataset. Comparative analysis against traditional CNN, LSTM, Bi-LSTM-DNN, and F-SFO-Bi-LSTM models demonstrated superior performance, especially in handling complex and similar Malayalam characters. Initial error analysis revealed misclassification issues for closely resembling characters, which the Bi-LSTM component effectively mitigates.
99.75%
Accuracy achieved by the proposed DSC-Bi-LSTM model for Malayalam character recognition.
| Feature | Traditional CNN | DSC-Bi-LSTM |
|---|---|---|
| Parameter Size Reduction | Standard operations | Significantly reduced (6x less than normal CNN) |
| Computational Cost | Higher | Lower through factorization |
| Misclassification Handling | Struggles with visually similar characters (Figure 23) | Improved with Bi-LSTM sequence learning model |
| Performance on Complex Scripts | Limited | High (99.75% for 90 Malayalam classes) |
The proposed DSC-Bi-LSTM model offers significant advantages in efficiency and accuracy, particularly for complex scripts with high character similarity, by reducing parameter size and leveraging sequence learning.
Enterprise Process Flow
Data Collection
→
Data preparation
→
Feature Extraction
→
Character Classification
→
Classification Result
Impact of Data Augmentation on Model Performance
The study demonstrates the critical role of data augmentation in achieving high accuracy for Malayalam Handwritten Character Recognition (MHCR). By applying translation, rotation, scaling, and elastic deformation, the self-generated dataset was expanded significantly. Translation proved to be the most effective augmentation technique, boosting the model's accuracy to 99.51% (Table 2). This process transformed a basic dataset of 18,000 samples into a robust 162,000-sample training set (Dataset-II), enabling the DSC-Bi-LSTM model to achieve an overall 99.75% accuracy on 90 complex Malayalam character classes.
162,000 samples
Augmented Dataset Size
1.4%
Translation Accuracy Boost
99.75%
Final MHCR Accuracy (Augmented)
Projected Efficiency & Cost Savings
Estimate the potential impact of an optimized HCR solution on your operational costs and resource allocation.
Your AI Implementation Roadmap
A strategic overview of the phased approach to integrate advanced HCR into your enterprise workflows.
Phase 1: Discovery & Data Assessment
Initial data collection, detailed analysis of existing data sources, preprocessing strategy formulation, and custom dataset creation for specific character sets.
Phase 2: Model Adaptation & Training
Tailoring the DSC-Bi-LSTM architecture to enterprise needs, implementing data augmentation techniques, and iterative training/fine-tuning for optimal accuracy.
Phase 3: Integration & Deployment
Developing robust APIs, seamlessly integrating the HCR solution with existing enterprise systems, and conducting pilot deployments in controlled environments.
Phase 4: Performance Monitoring & Iteration
Establishing continuous monitoring protocols, real-time accuracy refinement based on operational feedback, and scaling the solution across various business units.
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