PDF Annotation Tool

48 reviews

Jupyter notebook for PDF Annotation

OCR

3 reviews

Objectways Technologies offers a comprehensive Optical Character Recognition (OCR solution designed to convert various types of documents—such as scanned papers, PDFs, or images—into editable and searchable data. This service leverages advanced machine learning algorithms to accurately extract text from complex layouts, including tables and multi-column formats, ensuring high fidelity in data conversion. Key Features and Functionality: - High Accuracy: Utilizes state-of-the-art machine learning models to achieve precise text extraction, even from documents with intricate designs. - Multi-Language Support: Capable of recognizing and processing text in multiple languages, catering to a diverse user base. - Structured Data Extraction: Effectively identifies and extracts structured data elements like tables, forms, and key-value pairs, preserving the original document's format. - Scalability: Designed to handle large volumes of documents efficiently, making it suitable for enterprises with extensive data processing needs. - Integration Capabilities: Seamlessly integrates with existing workflows and systems, facilitating smooth adoption and minimal disruption. Primary Value and User Solutions: The OCR solution from Objectways Technologies addresses the challenge of manual data entry and document digitization by automating the extraction process. This automation significantly reduces processing time and minimizes human errors, leading to improved operational efficiency. Organizations can leverage this service to unlock valuable insights from previously inaccessible data, enhance searchability, and streamline document management processes. By converting unstructured documents into structured, actionable data, businesses can make informed decisions faster and more accurately.

Spline Video Labeling

0 reviews

Spline Video Labeling is a Jupyter Notebook application designed to facilitate the preparation and annotation of video data for machine learning tasks. It enables users to efficiently split input videos into frames, generate input manifests, and create video templates compatible with Amazon SageMaker Ground Truth workflows. Key Features and Functionality: - Data Preparation: Automatically splits input videos into individual frames, simplifying the process of preparing video data for analysis. - Manifest Generation: Creates input manifests required for Amazon SageMaker Ground Truth, streamlining the integration of video data into machine learning pipelines. - Video Template Creation: Develops video templates tailored for SageMaker Ground Truth workflows, facilitating efficient labeling and annotation processes. - Ease of Installation: Designed for straightforward deployment, allowing users to set up and begin using the tool with minimal effort. Primary Value and Problem Solved: Spline Video Labeling addresses the challenges associated with preparing and annotating video data for machine learning applications. By automating the extraction of frames and the creation of necessary input files, it significantly reduces the time and effort required for data preparation. This efficiency enables data scientists and machine learning practitioners to focus more on model development and less on the labor-intensive tasks of data preprocessing and annotation.

Spline Labeling - Multiple Curves

0 reviews

Product Description: Spline Labeling - Multiple Curves is a Jupyter Notebook application designed to facilitate the creation of multiple configurable splines and hierarchical labels within images. This tool is particularly beneficial for machine learning practitioners and data annotators who require precise and efficient labeling capabilities. By leveraging this application, users can enhance the quality of their datasets, leading to more accurate model training and improved performance in tasks such as object detection and image classification. Key Features and Functionality: - Spline Keypoints: Enables the creation and manipulation of multiple splines, allowing for detailed and flexible annotation of image features. - Hierarchical Labels: Supports the assignment of hierarchical labels, facilitating structured and organized annotation processes. - Machine Learning Integration: Designed to seamlessly integrate with machine learning workflows, enhancing the efficiency of data preparation and annotation tasks. Primary Value and Problem Solved: Spline Labeling - Multiple Curves addresses the challenge of efficiently creating detailed and structured annotations in image datasets. By providing tools for configurable splines and hierarchical labeling, it streamlines the annotation process, reducing manual effort and minimizing errors. This leads to higher-quality datasets, which are crucial for training accurate and reliable machine learning models.

Data Labeling Services by Objectways

0 reviews

Our core data labeling capabilities include: 3D Point Cloud: Object Detection, Object Tracking, Semantic Segmentation Video: Object Detection, Object Tracking, Clip Classification Images: Image Classification, Object Detection, Semantic Segmentation Text: Text Classification, Named Entity Recognition Audio: Transcription, Translation

Hierarchical annotations

0 reviews

Hierarchical Annotations is a sophisticated data labeling solution designed to streamline the creation of complex, multi-level annotations for machine learning datasets. By enabling the structuring of labels in a hierarchical manner, it facilitates the development of more accurate and context-aware machine learning models. Key Features and Functionality: - Multi-Level Labeling: Supports the creation of nested annotations, allowing for detailed and organized labeling structures. - Flexible Taxonomies: Enables the definition of custom hierarchical label taxonomies tailored to specific project requirements. - Integration with AWS Services: Seamlessly integrates with Amazon SageMaker Ground Truth and AWS Step Functions to automate complex labeling workflows. - Quality Assurance Mechanisms: Incorporates automated quality checks and human review steps to ensure high-quality annotations. - Scalability: Designed to handle large datasets efficiently, making it suitable for enterprise-level machine learning projects. Primary Value and Problem Solved: Hierarchical Annotations addresses the challenge of creating detailed and structured labels for complex datasets, which is crucial for training high-performing machine learning models. By automating and organizing the labeling process, it reduces manual effort, minimizes errors, and enhances the overall quality of the training data. This leads to more accurate model predictions and improved performance in real-world applications.

Language Video Labeling

0 reviews

Language Video Labeling is a specialized service designed to facilitate the annotation of video content for machine learning applications. It enables users to efficiently classify videos and label video frames, streamlining the creation of high-quality training datasets. Key Features and Functionality: - Video Classification: Allows users to assign predefined labels to entire video clips, aiding in tasks such as categorizing content by genre or subject matter. - Video Frame Object Detection: Enables the identification and localization of objects within individual video frames using bounding boxes, polylines, polygons, or keypoints. - Video Frame Object Tracking: Facilitates the tracking of objects across multiple frames, capturing their movement and interactions over time. - Automated Frame Extraction: Supports the extraction of frames from video files, simplifying the preparation of data for labeling tasks. - Integration with Amazon SageMaker Ground Truth: Provides a user-friendly interface and tools for managing labeling jobs, including worker instructions and task templates. Primary Value and Problem Solved: Language Video Labeling addresses the challenge of creating accurately labeled video datasets, which are essential for training machine learning models in applications like autonomous driving, sports analytics, healthcare diagnostics, and manufacturing. By offering a comprehensive suite of labeling tools and workflows, it reduces the time and effort required to annotate video data, thereby accelerating the development and deployment of machine learning solutions.