Bounding box identification for page segmentation and OCR

Region Proposal Networks (RPNs), also known as bounding box AI models, are becoming increasingly important in document AI as they greatly improve the efficiency and accuracy of information extraction.

In this blog post, we explore why bounding box AI models are essential for document AI, present five recent research papers and demonstrate the capabilities of the Konfuzio SDK for extracting data and bounding boxes to train your models.

What are bounding boxes?

Bounding boxes are imaginary rectangles used in image processing for object detection and collision detection. Data annotators draw these rectangles around key objects in images and define the X and Y coordinates to help machine learning algorithms efficiently find collision paths. Multiple bounding boxes and data enhancement methods are used together for better prediction rates.

Bounding Box OCR
See documentation on dev.konfuzio.com

Important parameters defining a bounding box are the class (object type), (X0, Y0) and (X1, Y1) for the upper left and lower right corners, (X1, Y1) for the center, width, height, and confidence (probability of the object inside the box). Two main conventions are used to specify a bounding box: X and Y coordinates of the upper left and lower right points, or X and Y coordinates of the center along with width and height. Bounding boxes are efficient and inexpensive image annotation techniques.

Impact of Bounding Boxes on Document AI.

Document AI involves various tasks such as OCR, text extraction, and information classification, making bounding box AI models an essential part of the process. Bounding boxes offer several advantages:

  1. Accurate text localization: Bounding boxes enable precise localization of text elements within a document, which is critical for correct extraction and classification.
  2. Complex layout processing: Documents often have complicated layouts with multiple columns, tables, and images. Bounding box AI models effectively segment these elements and enable more accurate data extraction.
  3. Improved OCR performance: Bounding box AI models improve OCR performance by focusing on specific areas of interest, reducing false positives, and increasing recognition accuracy.
  4. Improved data extraction: Bounding box AI models facilitate the extraction of relevant data from documents by identifying and segmenting specific text elements such as names, dates, and addresses.
  5. Scalability: Because bounding box AI models are based on deep learning techniques, they can be adapted to new and different document types with minimal manual intervention, making them highly scalable for large-scale Document AI applications.

Annotation datasets for machine learning models

Datasets with annotations play a critical role in developing machine learning models, especially for image-based tasks. By providing annotated images with bounding boxes surrounding objects of interest, developers can create comprehensive datasets that help models recognize patterns and associations between object classes and features. These datasets form the basis for training various deep learning models, including neural networks for object recognition and classification.

Neural networks and computer vision applications

Neural networks, particularly deep learning models such as Convolutional Neural Networks (CNNs), have revolutionized computer vision applications. The goal of these applications is to teach machines to interpret and understand visual information from the world. By automatically learning features and patterns from images, these models eliminate the need for manual feature creation. By using annotated bounding boxes during the training process, neural networks can efficiently learn to locate and identify objects in images, leading to significant advances in Document AI and other areas of computer vision.

Include bounding boxes in object recognition models

The integration of bounding boxes into object recognition models such as YOLO, SSD, and Faster R-CNN is essential for their training. These models use annotated datasets containing bounding boxes to learn how to predict object positions and classes in images. During the training process, the object recognition models use these annotations to optimize their parameters, resulting in improved prediction accuracy. Once trained, these models can generate bounding boxes around objects in new unseen images, enabling efficient and accurate information extraction in various applications, including Document AI.

Image and document datasets

Image data sets

Numerous image and document datasets can be used for training neural image processing models, including:

  1. COCO (Common Objects in Context): A widely used dataset that contains 330,000 images with annotations for 80 object classes and focuses on object recognition, segmentation, and labeling tasks.
  2. Pascal VOC: A popular dataset for object detection and segmentation that includes 11,530 images with annotations for 20 object classes.
  3. Open Images: A rich dataset of 9 million images and annotations for over 600 object classes suitable for object recognition, segmentation, and visual relationship detection tasks.
  4. ADE20K: A dataset for scene parsing containing 20,210 images with annotations for 150 object classes useful for semantic segmentation tasks.

Document records

  1. RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing): A dataset of 400,000 grayscale document images with annotations for 16 document categories, suitable for document classification tasks.
  2. ICDAR (International Conference on Document Analysis and Recognition): A set of datasets published in conjunction with the ICDAR conference that focus on tasks such as text recognition, recognition, and segmentation in document images.
  3. PubLayNet: A comprehensive dataset of over 360,000 document images and annotations for five common layout elements (text, title, list, table, and figure) designed for document layout analysis and segmentation.
  4. FUNSD (Form Understanding in Noisy Scanned Documents): A dataset of 199 scanned forms with annotations for form understanding tasks, including text recognition, key-value pair extraction, and form field segmentation.
  5. DocBank: A rich dataset of 500,000 document images annotated to 13 categories and fine-grained token-level information designed for document layout analysis and information extraction.

These datasets cover various aspects of image and document processing, providing a solid foundation for training neural vision models in diverse computer vision and document AI tasks.

Research papers on bounding box AI models.

  1. "EfficientDet: Scalable and Efficient Object Detection" by Mingxing Tan, Ruoming Pang, and Quoc V. Le.
  2. "Cascade R-CNN: High-Quality Object Detection and Instance Segmentation" by Zhaowei Cai and Nuno Vasconcelos.
  3. "YOLOv4: Optimal speed and accuracy of object detection" by Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao.
  4. "FCOS: Fully Convolutional One-Stage Object Detection" by Zhi Tian, Chunhua Shen, and Hao Chen.
  5. "DETR: End-to-End Object Detection with Transformers" by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko.

Konfuzio SDK - Data Retrieval and Bounding Box Training

The Konfuzio SDK provides a comprehensive solution for retrieving data and bounding boxes from documents, allowing you to effectively train your models. Key features of the Konfuzio SDK include:

  1. Data extraction: With the SDK you can extract text, images, tables and other elements from documents with high accuracy.
  2. Bounding box generation: It enables the creation of precise bounding boxes around text elements, facilitating accurate data extraction and classification.
  3. Custom model training: The SDK supports training of custom models using your labeled data, ensuring better performance and adaptability to your specific use case.
  4. Integration with popular frameworks: The Konfuzio SDK integrates seamlessly with popular deep learning frameworks such as TensorFlow and PyTorch, so you can take advantage of the latest research and techniques.
  5. Continuous improvement: The SDK enables continuous improvement of your models through active learning, ensuring that your document AI system stays current and meets changing requirements.

Challenges and future prospects in bounding box prediction.

Bounding box AI models have transformed the field of document AI by providing accurate and efficient page segmentation capabilities. The latest research shows the continuous progress in this area.

Despite significant progress in bounding box prediction and its applications in document AI, there are still challenges. One of these challenges is accurately predicting bounding boxes for highly cluttered or overlapping objects. In addition, the performance of object recognition models depends heavily on the quality and quantity of the annotated datasets.

As the need for more accurate and efficient document AI systems increases, future research will likely focus on addressing these challenges by developing innovative techniques for improved bounding box prediction, using unsupervised or semi-supervised learning, and creating more diverse and extensive datasets for training purposes.

New techniques, such as "few-shot learning" and "transfer learning," show promise for reducing reliance on large annotated datasets or learning continuously from human feedback, see our paper Human-in-the-loop. These approaches can help reduce the burden of manual annotation and allow models to better generalize across different document types and layouts.

In addition, integrating natural language processing (NLP) techniques with bounding box AI models can help improve the understanding of context and semantics in documents. This synergy can lead to smarter information extraction and classification, allowing Document AI systems to better understand and process complex documents.

Another area of research that is expected to contribute to the advancement of bounding-box AI models is the advancement of hardware and software optimization techniques. As deep learning models become more complex and computationally intensive, improving the efficiency of bounding box predictors will be critical. Innovations in hardware, such as GPUs and specialized AI chips, along with software optimizations and algorithmic advances, will play an important role in the continued progress of Document AI.

In summary, the future of document AI appears promising as researchers and developers continue to push the boundaries of what is possible with bounding box AI models and related techniques. As these technologies continue to evolve, we can expect even more accurate, efficient, and adaptable document AI systems capable of addressing a wide range of tasks and challenges in various industries and domains.

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Elizaveta Ezhergina Avatar

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