Sign Language Translator AI (SLTA)

This milestone focuses on building a comprehensive sign language dataset that captures the diversity and complexity of sign language gestures. The work encompasses gathering data from multiple sources and ensuring sufficient variety in terms of demographics environmental conditions and signing styles. This foundation is crucial for developing a robust and inclusive sign language recognition system.

- Collection and aggregation of sign language datasets from various sources (e.g. Kaggle ASL alphabet dataset) to ensure diverse representation across races genders lighting conditions and other variations - Verify proper consent and ethical data collection for all sign language video datasets, ensuring diverse representation while protecting participant privacy - Implementation of data augmentation techniques to expand the dataset through controlled variations such as slight rotations scale adjustments flipping and noise addition to enhance model robustness - Creation of supplementary dataset through custom recording sessions if external datasets prove insufficient for comprehensive coverage - Organization of the complete dataset into structured training (75%) validation (15%) and test (15%) sets ensuring balanced representation across all sign categories

$15,000 USD

- Comprehensive labeled dataset with clear gloss annotations for each sample, organized in a structured folder hierarchy - Balanced representation across different demographic groups and environmental conditions

This milestone addresses the transformation of raw sign language video data into a structured format suitable for machine learning. The focus is on extracting meaningful spatial and temporal features from the sign language gestures while ensuring consistency and standardization across all samples creating a foundation for effective model training.

- Extraction of hand landmarks from video frames using MediaPipe capturing comprehensive spatial information including key points for fingers palm and other critical hand components - Implementation of coordinate normalization to create a uniform reference system with palm-centric coordinates where the palm center serves as the origin (0 0) using MediaPipe for consistent processing - Processing of dynamic signs into standardized sequence lengths to capture temporal transitions between hand movements with flexibility for adjustment based on sign complexity and speed - Creation of structured data formats suitable for model input including sequential storage of landmark coordinates (x y z) for each frame in accessible formats such as numpy arrays or CSV files

$20,000 USD

- Complete extraction of landmarks for all video frames with proper CSV file storage - Verified normalization of landmark coordinates using palm-centered anchoring - Ready-to-use dataset format suitable for model training

This milestone encompasses the development and optimization of the sign language recognition model. The focus is on implementing and training a sequence-based deep learning architecture capable of accurately recognizing continuous sign language gestures with particular attention to handling the temporal aspects of signing.

- Selection and implementation of appropriate sequence-based model architecture evaluating options including Long Short-Term Memory (LSTM) RNN and transformer models based on project requirements - Implementation of Connectionist Temporal Classification (CTC) loss function to handle variable sequence lengths and misalignments between input video frames and output glosses/words - Comprehensive hyperparameter optimization through grid search or random search including learning rate batch size sequence length and model architecture parameters - Implementation of Word Error Rate evaluation metric to assess sequence-level prediction accuracy rather than frame-level performance - Check training progress to maintain similar performance levels across demographic categories - Development of model saving and loading functionality with verification of successful model restoration and inference

$10,000 USD

- Detailed model performance report with comprehensive metrics - Verified model saving and loading functionality with successful inference testing

This milestone focuses on making the trained sign language recognition model accessible and operational within the SingularityNET ecosystem while ensuring that safety, privacy, and ethical considerations are rigorously addressed. We will ensure no user data is stored or retained during the translation process.

- Full integration of privacy measures to ensure no user data is stored or retained. This includes processing all interactions with the service in real time without retaining any sensitive or personal information. - Ensuring that no personally identifiable information (PII) is captured, stored, or misused. - Ensuring inclusivity by using a diverse dataset representing various groups to avoid biases in the sign language translation. This ensures equitable access and accurate translations for all users. - Complete service configuration setup and deployment of the sign language recognition service within the SingularityNET platform. - Publication of the service to the SingularityNET marketplace, ensuring proper accessibility for both users - Execution of comprehensive testing including functionality verification, performance validation, reliability testing, and integration testing with other platform services to ensure robust operation

$5,000 USD

- Successful deployment and accessibility verification on SingularityNET - Complete resolution of all testing-identified issues - Smooth operation within the SingularityNET environment