Scanned documents sometimes can have pages with wrong alignment. This can create challenges while processing of documents viz. OCR, ICR, Text extraction, image-based ML/AI modelling, etc. This solution incorporates statistical models which identify angle of tilt based on textual orientation, position of text relative to page boundaries and text clusters and corrects the alignment / tilt of the pages. This enables OCR / ICR engines to achieve higher accuracy and improves the subsequent text extraction pipelines.

DeepInsights Text Paraphraser helps in re-expressing the text content in a different style without changing the original meaning. The solution can be used to get a better understanding of the data and simplify complex sentences. Transformer based models are used which helps in retaining the contextual meaning.

The PCB Defect Detector is an advanced machine learning application designed to identify and classify defects in printed circuit boards (PCBs during the manufacturing process. By leveraging computer vision and artificial intelligence, it automates the inspection of PCBs, ensuring high-quality standards and reducing reliance on manual inspections. Key Features and Functionality: - Automated Defect Detection: Utilizes machine learning models to detect various PCB defects, including missing components, soldering issues, and surface anomalies. - High Accuracy: Employs advanced algorithms to achieve precise identification of defects, minimizing false positives and negatives. - Scalability: Capable of handling high volumes of PCB inspections, making it suitable for large-scale manufacturing operations. - User-Friendly Interface: Features an intuitive interface that allows operators with minimal technical knowledge to effectively use the system. - Integration with AWS Services: Seamlessly integrates with AWS services such as Amazon SageMaker and AWS Lambda for model training, deployment, and inference. Primary Value and Problem Solved: The PCB Defect Detector addresses the challenges of manual PCB inspections, which are often time-consuming and prone to human error. By automating the defect detection process, it enhances inspection accuracy, reduces operational costs, and accelerates production cycles. This leads to improved product quality and increased customer satisfaction, while also allowing manufacturers to allocate human resources to more complex tasks.

This solution automatically identifies and trains the best performing deep learning model for text classification.