INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
CNN Approach for Static Hand Gesture Recognition in Indian Sign  
Language  
1Mr. Ronak Jitendrabhai Goda, 2Prof. Dr. C.K. Kumbharana  
1Research Scholar, Department of Computer Science, Saurashtra University, Rajkot, India  
2Professor, Department of Computer Science, Saurashtra University, Rajkot, India  
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.101100049  
Received: 04 December 2025; Accepted: 09 December 2025; Published: 10 December 2025  
ABSTRACT  
Indian Sign Language (ISL) plays a crucial role in bridging the communication gap between individuals who  
are hearing-impaired and the broader society. However, limited research and technological solutions exist for  
recognising ISL, especially in regional contexts. This paper presents a deep learning-based approach for  
recognising static hand gestures that represent the ISL alphabet (A–Z). A Convolutional Neural Network (CNN)  
model is trained on a publicly available dataset containing labelled hand sign images. The system classifies input  
images into corresponding alphabetic characters with high accuracy, providing a real-time, low-cost, and  
accessible solution. The aim is to support inclusive human-computer interaction and assistive technology for the  
hearing-impaired community. The experimental results demonstrate the effectiveness of the proposed model,  
making it suitable for educational tools, basic communication aids, and future integration into mobile or web  
applications.  
Keywords: Indian Sign Language, CNN, Deep Learning, Hand Gesture Recognition, Accessibility, Alphabet  
Detection, Assistive Technology.  
INTRODUCTION  
Communication is a fundamental aspect of human life, enabling individuals to express thoughts, emotions, and  
information. For the hearing and speech-impaired community, Sign Language serves as a primary mode of  
communication. Among various sign languages used across the world, Indian Sign Language (ISL) is widely  
adopted in India. However, due to a lack of awareness and limited use of sign language by the general public,  
individuals who rely on ISL often face barriers in everyday communication.  
Recent advancements in artificial intelligence (AI) and deep learning have made it possible to bridge this  
communication gap by recognising hand gestures and translating them into readable or audible forms.  
Particularly, Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image  
classification tasks and have proven effective in identifying hand gestures from images.  
Technological solutions that can interpret sign language into text or speech in real-time are vital for improving  
accessibility. While research in American Sign Language (ASL) and other global systems has progressed,  
Indian Sign Language remains underexplored, particularly in terms of automated gesture recognition. In this  
context, computer vision and deep learning technologies present an effective solution.  
Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), have  
significantly improved the performance of image-based recognition systems. CNNs are well-suited for detecting  
and classifying patterns in images and have been widely used in facial recognition, object detection, and medical  
imaging. Their layered architecture allows them to learn spatial hierarchies of features, which is particularly  
useful in identifying complex hand gestures.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
This research aims to develop a CNN-based system for recognising static hand gestures corresponding to the  
ISL alphabet (A–Z). The system is trained using a publicly available dataset of hand gesture images and is  
capable of classifying each input image into the correct alphabetic label. The approach is simple, cost-effective,  
and requires minimal hardware, making it suitable for educational tools, digital classrooms, and basic assistive  
applications.  
Moreover, the proposed system can be extended to generate real-time predictions from camera input, and future  
versions could include voice synthesis modules for enhanced communication. By focusing on static gestures,  
the model remains lightweight and avoids the complexity of temporal sequence modelling required for dynamic  
gesture recognition.  
This paper focuses on the development of a CNN-based system for recognising static hand gestures  
representing the ISL alphabet (A–Z). The proposed system uses a dataset of labelled hand sign images to train  
a deep learning model capable of classifying gestures into corresponding alphabetic characters. Unlike complex  
dynamic gesture systems that require video processing, this system works with static images, making it more  
accessible and computationally efficient.  
The objective of this research is to design a low-cost, accurate, and real-time system that can assist hearing-  
impaired users in communicating basic textual information through hand gestures. The system can be further  
extended to voice output using speech synthesis and integrated into mobile or desktop platforms.  
LITERATURE REVIEW  
Indian Sign Language (ISL) recognition has gained increasing attention in recent years, particularly through the  
application of deep learning methods such as Convolutional Neural Networks (CNNs). Mishra et al. [1]  
presented a CNN-based system to classify ISL static hand gestures for the alphabet A–Z, achieving  
approximately 92% accuracy on a custom dataset. Their work demonstrated the feasibility of using CNNs for  
basic sign language tasks in controlled environments.  
Building upon this, Kumar and Raj [2] developed a lightweight CNN model trained on grayscale gesture images.  
Despite using a relatively simple 3-layer architecture, their approach performed effectively, making it suitable  
for low-power or embedded devices. However, the model lacked robustness against background and lighting  
variations.  
In an effort to create real-time solutions, Sarkar et al. [3] designed a live ISL interpreter using a webcam and  
OpenCV integrated with a CNN. While the system could predict hand signs in real time, its accuracy dropped  
significantly in dynamic environments, indicating a need for improved preprocessing and background filtering.  
Ghosh and Basu [4] conducted a comparative study between conventional CNNs and transfer learning models  
like MobileNetV2 and VGG16. Their results showed that while transfer learning offered slightly higher accuracy  
(around 95%), it required significantly more computational resources. They concluded that custom CNNs are  
more appropriate for low-resource settings such as classrooms or mobile deployment.  
Prajapati et al. [5] explored traditional image processing techniques such as contour detection, skin color  
segmentation, and background subtraction to classify static hand gestures. Though innovative at the time, the  
approach delivered lower accuracy (~70%) and performed poorly in uncontrolled conditions, highlighting the  
limitations of non-deep-learning-based methods.  
Jain and Patel [6] addressed the issue of limited dataset size by applying various data augmentation techniques,  
such as rotation, flipping, and brightness adjustments. Using a 4-layer CNN, their model achieved over 95%  
accuracy, showing that synthetic dataset expansion can greatly improve generalization performance.  
Verma et al. [7] emphasized the role of preprocessing in gesture recognition. They applied background  
elimination techniques before training their CNN, which resulted in faster convergence and improved  
classification performance, especially in visually noisy environments.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Roy et al. [8] compared CNN and Support Vector Machine (SVM) classifiers using the same ISL alphabet  
dataset. The study found that CNNs outperformed SVMs in terms of both accuracy and robustness, supporting  
the idea that deep learning models are better suited for gesture classification tasks.  
Kaur et al. [9] combined CNN recognition with a GUI application built using Tkinter, allowing users to upload  
gesture images and receive predictions in real time. Their system was well-received in educational settings and  
served as a low-cost, assistive tool for teaching ISL.  
Lastly, Yadav and Dubey [10] proposed a multi-scale CNN model that could extract features at different spatial  
resolutions. This enabled the network to better distinguish between visually similar gestures, such as “M” and  
“N.” Their model achieved high precision and recall scores, indicating the benefit of using multi-resolution  
feature extraction.  
Author(s)  
Year Technique / Model  
Dataset Size  
Accuracy (%)  
Key Remarks  
Mishra et al. 2020 CNN (custom)  
[1]  
26 classes, 7,800 92.0  
images  
Basic  
recognition,  
alphabet  
limited  
lighting variation  
Kumar & Raj 2019 Lightweight CNN  
[2]  
26 classes, 5,000 90.4  
images  
Low-power  
implementation  
device  
Sarkar et al. 2021 CNN  
+
OpenCV 24 classes  
88.5  
Accuracy  
drop  
in  
[3]  
(real-time)  
dynamic background  
Ghosh  
Basu [4]  
& 2022 VGG16  
MobileNetV2  
/ 10,000 images  
95.0  
High performance but  
resource-heavy  
Jain & Patel 2021 4-layer  
[6] Augmentation  
CNN  
+ 26 classes, 8,500 95.2  
images  
Data  
improved  
augmentation  
generalization  
Verma et al. 2020 CNN + Background 26 classes, 9,100 94.6  
Faster  
convergence  
[7]  
Elimination  
images  
and improved clarity  
Kaur & Gupta 2021 CNN  
+
GUI 26 classes  
93.8  
Interactive,  
user-  
[9]  
(Tkinter)  
friendly system  
Balanced  
accuracy  
between  
&
Proposed  
(This Work)  
2025 5-layer  
(custom)  
CNN 26 classes, 13,000 93.0  
images  
computation cost  
(augmented)  
Proposed System  
The proposed system comprises three main modules: Dataset Used, Preprocessing, and CNN Model.  
Dataset Used  
The dataset contains images representing the 26 ISL alphabet gestures. Images are captured under varied  
lighting, hand orientations, and backgrounds. To improve performance, data augmentation techniques such as  
rotation, flipping, and brightness adjustment were applied. All images were resized to 64x64 pixels and  
converted to grayscale.  
Preprocessing  
Key preprocessing steps include:  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Resizing images to 64x64 pixels  
Grayscale conversion  
Normalisation to [0, 1] range  
Data augmentation using rotation, flip, zoom, and brightness  
These steps enhance the robustness of the system under real-world conditions.  
CNN Model Architecture  
Layer Type  
Input Layer  
Conv Layer 1  
Configuration  
64x64x1 grayscale image  
32 filters, 3x3 kernel, ReLU  
Max Pooling Layer 1 2x2  
Conv Layer 2  
64 filters, 3x3 kernel, ReLU  
Max Pooling Layer 2 2x2  
Flatten Layer  
Dense Layer  
Dropout Layer  
Output Layer  
Converts 2D to 1D  
128 neurons, ReLU  
0.3 dropout rate  
26 neurons, Soft max activation (A–Z classes)  
Implementation and Results  
This section outlines the implementation details of the proposed system, including the tools used, system  
configuration, training parameters, and performance metrics. The results demonstrate the effectiveness of the  
CNN-based approach for recognizing static ISL alphabet gestures.  
Tools and Environment  
The system was implemented using the following tools and libraries:  
Programming Language: Python 3.x  
Libraries/Frameworks: TensorFlow, Keras, OpenCV, NumPy, Matplotlib  
Development Platform: Jupyter Notebook / Google Colab  
Operating System: Windows 10 / Linux (Ubuntu)  
Hardware: Intel i5/i7 processor, 8–16 GB RAM, with/without GPU (NVIDIA CUDA support  
recommended)  
Model Training Parameters  
The CNN model was trained on the preprocessed ISL dataset using the following settings:  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Parameter  
Image size  
Batch size  
Value  
64 × 64  
32  
Number of epochs  
Optimizer  
25  
Adam  
Loss function  
Categorical Cross-Entropy  
Activation (hidden) ReLU  
Activation (output)  
Validation split  
Softmax  
20%  
Accuracy metric  
Categorical Accuracy  
Data augmentation was applied in real time during training using Keras' Image Data Generator.  
Performance Evaluation  
The trained model was evaluated using accuracy, loss, and confusion matrix. Key results are as follows:  
Training Accuracy: ~97%  
Validation Accuracy: ~94%  
Loss Convergence: Smooth convergence after ~20 epochs  
Test Accuracy: ~93% (on unseen test set)  
A sample confusion matrix showed that commonly confused classes were visually similar gestures such as ‘M’  
and ‘N’ or ‘U’ and ‘V’.  
Visualization of Results  
Below are sample training graphs:  
Accuracy vs. Epochs: Training and validation accuracy showed upward trends with minimal overfitting.  
Loss vs. Epochs: Training and validation loss decreased steadily, indicating good generalization.  
Sample Outputs: Gesture images were correctly classified with high confidence scores. Incorrect  
predictions were mostly due to poor lighting or unusual hand angles.  
Challenges and Limitations  
While the proposed CNN-based system shows promising results, it also has certain limitations. The system is  
limited to static gestures and does not address dynamic gestures such as 'J' and 'Z', which require temporal  
modeling. In addition, the dataset is limited in diversity regarding backgrounds, hand sizes, and skin tones, which  
may affect generalization in real-world conditions. Real-time deployment also depends on device capabilities,  
and performance can degrade on low-resource systems without GPU acceleration.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
Application Scenarios  
The proposed model has practical utility in several domains:  
- Educational tools for teaching ISL in schools  
- Mobile applications for real-time gesture-to-text translation  
- Communication aids for deaf or mute individuals  
- Integration in public kiosks or government centres for accessibility  
- Basic command recognition in smart devices  
DISCUSSION  
The CNN-based system achieved a strong test accuracy of 93%, demonstrating its capability to learn spatial  
features of static hand gestures effectively. The introduction of data augmentation proved beneficial, improving  
accuracy by nearly 4–5% compared to non-augmented training.  
Misclassifications were mostly observed between similar-shaped alphabets (e.g., “M” vs “N” and “U” vs “V”),  
which share closely related finger postures. Incorporating finer contour extraction or key-point-based hand  
segmentation (e.g., Media Pipe) could further reduce these overlaps.  
The confusion matrix analysis also highlights that lighting and background variations have a moderate impact  
on performance. This suggests that future models should integrate adaptive histogram equalisation or  
background subtraction to improve robustness.  
Despite using a simple 5-layer CNN, the system achieved results comparable to heavier transfer-learning models,  
confirming its suitability for low-resource devices such as Raspberry Pi or Android smartphones. The model’s  
modest parameter count enables near real-time inference (~20–30 fps on mid-range GPUs).  
CONCLUSION  
This paper presented a CNN-based system for recognising static hand gestures of the Indian Sign Language  
(ISL) alphabet. The system uses a structured dataset, effective image preprocessing techniques, and a custom  
CNN model to classify 26 alphabet gestures (A–Z) with high accuracy. Experimental results show that the model  
achieves over 93% accuracy on the test set, demonstrating its potential for practical deployment.  
Compared to existing studies, the proposed method strikes an optimal balance between accuracy, computation  
cost, and deploy ability. Its modular design and lightweight structure make it well-suited for real-time  
applications in assistive technology, education, and communication interfaces for hearing- and speech-impaired  
users.  
Future work will focus on:  
Extending recognition to dynamic gestures using 3D-CNNs or LSTMs.  
Developing mobile/web prototypes for real-time translation.  
Enhancing generalization across different users, skin tones, and lighting.  
Integrating speech synthesis and multilingual translation modules.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025  
The modular design of the system makes it suitable for integration into assistive communication tools for the  
hearing- and speech-impaired communities. Its scalability and efficiency also enable deployment on low-cost  
devices such as smartphones and embedded boards.  
Future Work  
Although the system performs well for static gestures, several areas can be explored further:  
Dynamic Gesture Recognition: Extending the model to support real-time recognition of dynamic  
gestures, including full words or sentences.  
Real-Time Implementation: Integrating the system with a webcam or mobile camera for live gesture  
recognition.  
ISL Grammar Translation: Converting sequences of gestures into grammatically correct ISL  
sentences.  
Multilingual Support: Translating recognized ISL gestures into multiple spoken/written languages.  
User Feedback Loop: Adding interactive feedback for correcting or retraining the model with custom  
gestures.  
By addressing these areas, the system can be transformed into a complete, real-time ISL translation solution.  
REFERENCES  
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