Identifying American Sign Language Using Deep Learning

Abstract

Identifying American Sign Language Using Deep Learning Abstract Sign language conversion has been a long standing computer vision problem. Several solutions have come up but none of them have been portable for them to be used in a standalone device or application. We plan on alleviating this problem by harnessing the power of the mobile phone and the recent advances in deep learning. With the advent of deep learning, end to end models are being built for a wide range of problems that only require the images as input. Datasets have made it possible to harness the power of the models better. Imagenet is the best example, it is still driving innovation and advancements in computer vision. Another such dataset is the Microsoft Cocoa which is one of the benchmarks for image segmentation and human pose estimation. The problem of image classification has become very trivial now, on the other hand image segmentation is still quite difficult. We propose an end to end solution that would require only a 2D image as an input. For this we follow a 3 Segment approach inspired by. Our aim is to make it easy for people to communicate using the model. There are numerous people who use the ASL around the world. A vision based approach to our solution attempts to reduce the requirement of human translators and increase dependency on the user’s phone for translation. The research background is that communication is very important in the process of social interaction. Communication leads to better understanding among the community, including the deaf. Hand gesture recognition serves as the key to overcoming many difficulties and providing convenience for human life, especially for the deaf. Sign language is a structured form of hand movement that involves visual movement and the use of various body parts namely fingers, hands, arms, head, body, and facial expressions to convey information in the communication process. For the deaf and speech-impaired community, sign language serves as a useful tool for everyday interactions. However, sign language is not common among normal people, and only a few people understand sign language. This creates a real problem in communication between the deaf community and other communities, which has not been fully resolved to this day. Not all words have sign language, so special words that do not have sign language must be spelled using a letter sign one by one. Based on the background, this study aims to develop a sign language recognition model for letters of the alphabet using a deep learning approach. The deep learning approach was chosen because deep learning methods are popular in the field of computer science and are proven to produce a good performance for image classification. The novelty of this study is the application of resizing and background correction of the input image for training and testing to improve model performance, where the results of testing the model we propose are better than previous similar studies. American Sign Language (ASL) substantially facilitates communication in the deaf community. However, there are only ~250,000-500,000 speakers which significantly limits the number of people that they can easily communicate with. The alternative of written communication is cumbersome, impersonal and even impractical when an emergency occurs. In order to diminish this obstacle and to enable dynamic communication, we present an ASL recognition system that uses Convolutional Neural Networks (CNN) in real time to translate a video of a user’s ASL signs into text. Our problem consists of three tasks to be done in real time: 1. Obtaining video of the user signing (input) 2. Classifying each frame in the video to a letter 3. Reconstructing and displaying the most likely word from classification scores (output)

Specification

Description:CLAIMS

1. American Sign Language (ASL) is the primary language used by many deaf individuals in North America, and it is also used by hard-of-hearing and hearing individuals.
2. The language is as rich as spoken languages and employs signs made with the hand, along with facial gestures and bodily postures.

3. A lot of recent progress has been made towards developing computer vision systems that translate sign language to spoken language. This technology often relies on complex neural network architectures that can detect subtle patterns in streaming video. However, as a first step, towards understanding how to build a translation system, we can reduce the size of the problem by translating individual letters, instead of sentences.

4. In this notebook, I will train a convolutional neural network to classify images of American Sign Language (ASL) letters. After loading, examining, and preprocessing the data, I will train the network and test its performance.

5. In the code cell below, I load the necessary libraries, and training and test data directories.

6. American Sign Language (ASL) is a complete, natural language that has the same linguistic properties as spoken languages, with grammar that differs from English. ASL is expressed by movements of the hands and face. It is the primary language of many North Americans who are deaf and hard of hearing and is used by some hearing people as well.
, Claims:Description

Enhanced Recognition Accuracy:

● Deep learning models, particularly Convolutional Neural Networks (CNNs), excel at capturing complex patterns in visual data, leading to higher accuracy in gesture recognition.
● These models are proficient in distinguishing subtle differences in hand shapes and movements.

Strong Adaptability to Varied Conditions:

● Deep learning algorithms can adapt to different conditions like varied lighting, backgrounds, and individual signer differences, ensuring consistent performance.
● This adaptability is crucial for real-world applications of ASL recognition.

Effective Processing of Temporal Information:

● Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM) networks, can effectively interpret sequential data, capturing the dynamics of sign language over time.
● This ability is essential for understanding the context and grammar of ASL.

Automatic Feature Extraction:

● Deep learning models automatically extract and learn the most relevant features from raw data, reducing the need for manual feature engineering.

● This leads to a more efficient and scalable model development process.

● The system design for American Sign Language (ASL) recognition with deep learning, particularly using Convolutional Neural Networks (CNNs), is structured to effectively process and interpret sign language gestures captured in image or video format. CNNs, known for their ability to automatically learn hierarchical features from input data, are employed to extract meaningful features from ASL images or video frames. These features capture the spatial relationships and patterns present in the hand gestures, enabling the model to effectively recognize and classify different ASL signs. The CNN architecture typically consists of multiple layers such as convolutional, pooling, and fully connected layers, which collectively learn to identify key features and patterns in the input images. Additionally, techniques like data augmentation may be applied to increase the robustness of the model by generating variations of the training data, ensuring that the model can generalize well to unseen sign language gestures.

● Moreover, the system design incorporates preprocessing steps to enhance the quality of the input data and facilitate better model performance. This may include techniques such as image normalization, resizing, and noise reduction to standardize the input images and remove irrelevant information. Furthermore, the design may involve the use of transfer learning, where pre-trained CNN models on large image datasets are fine-tuned on ASL data to leverage the learned representations and accelerate the training process. Overall, the system design for ASL recognition with deep learning using CNNs aims to develop a robust and accurate model capable of interpreting sign language gestures with high precision, thereby facilitating effective communication for individuals with hearing impairments.
● In ASL (American Sign Language) recognition systems utilising deep learning with CNN (Convolutional Neural Network) techniques, the database design plays a crucial role in storing and managing the data necessary for training and evaluation. Typically, the database comprises two main components: the training dataset and the validation/test dataset. The training dataset consists of a large collection of ASL images or video frames, each labelled with the corresponding ASL sign. These labelled images serve as the input data for training the CNN model, allowing it to learn the underlying patterns and
● features associated with different ASL signs. The training dataset should be diverse and representative, covering a wide range of ASL gestures and variations in lighting conditions, backgrounds, and hand orientations to ensure the robustness of the trained model.

● In addition to the training dataset, the database includes a validation or test dataset used for evaluating the performance of the trained CNN model. This dataset contains unseen ASL images or video frames that were not used during the training phase. The validation/test dataset helps assess the generalization ability of the CNN model by measuring its accuracy and performance on unseen data. Proper database design ensures the integrity and organization of these datasets, facilitating efficient data retrieval, preprocessing, and augmentation during the training process. Moreover, it enables reproducibility and scalability, allowing researchers and developers to conduct experiments and fine-tune the CNN model architecture and hyperparameters to achieve optimal performance in ASL recognition tasks.

Documents