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9 Applications of Computer Vision in Law and Legal Systems

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Contents

The evolution of computer vision technology has paved the way for innovative artificial intelligence (AI) solutions in the legal industry. Beyond traditional applications like people detection, object tracking, and behavior analysis, computer vision has the potential to offer many more creative and nuanced solutions. In this article, we will explore 9 applications of computer vision in law and legal systems.

  • #1: Criminal Activity Detection
  • #2: Legal Review
  • #3: Courtroom Proceedings
  • #4: Intellectual Property (IP) Protection and Deepfakes
  • #5: Forensic Analysis
  • #6: Face Recognition in Legal Investigations
  • #7: Virtual Courtrooms
  • #8: Compliance Monitoring
  • #9: Evidence Authentication

Deep learning object detection has been developed by researchers and adopted by computer vision companies to build commercial products that law institutions can utilize. Some of the most significant object detectors are YOLO, SSD, RetinaNet, etc. They are based on two-stage object detectors, where approximate object regions are proposed using deep features before these features are used for the image classification and bounding box regression for the object candidate.

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Importance of Computer Vision in Law

Computer vision offers regulators a toolset for automating processes and navigating complex legal frameworks more effectively. By integrating these systems into the practice of law, regulators streamline enforcement processes and ensure alignment with ever-evolving legal standards.

 

1. Criminal Activity Detection

In criminal cases, CV algorithms should first recognize the environment and the setting. Computer vision surveils and analyzes images from different sources (edge devices, street cameras, business cameras, etc.). Then they proceed to understand the composition of the surrounding objects and distinguish between pedestrians and vehicles. The global look of the scene will help the algorithm capture details, including the color and shape of vehicles, license plates, signboards, storefronts, etc. Anything that cannot be detected in the images, is searched in the backend to help law institutions identify, track, and ultimately – uncover the lawbreakers.

 

Object-level recognition in an urgan environment with YOLOv7. This can be for monitoring environments to track criminal activity and infractions
Object-level recognition in an urban environment with YOLOv7

 

2. Legal Review

Massive volumes of documents typically mar the traditional legal review process. By introducing computer vision, specifically Optical Character Recognition (OCR) for detecting textual symbols, legal teams can cut down on potential risks such as language subjectivity, cost and time constraints, compliance, and privacy and security concerns

Law firms and institutions organize and prioritize documents based on their relevance or specific criteria. CV algorithms can accurately categorize documents by analyzing document characteristics including structures, layout, and formatting. When paired with predictive analytics, computer vision systems can identify relevant patterns, relationships, and trends within the data. This automated extraction helps save legal practices and document review teams hundreds of hours of legal work normally spent scanning each document to identify these details.

 

Handwritten content detection for legal review with computer vision
Handwritten content detection – source.

 

How is this done? N. Huber-Fliflet, et al. applied deep learning R-CNN for document classification and clustering. For training data, they utilized 235 Google images of documents with handwriting, including handwriting notes, signatures, and hand-filled forms. They applied AI tools to label the handwriting regions, as rectangular boxes surrounding the region with class names. The labels have been saved in an XML file in Pascal VOC format for each of the images. Then they converted the XML files into a single CSV file and then split it into training and testing sets.

Finally, the training and testing data were converted into TFRecords to feed into the learning algorithm.

 

3. Courtroom Proceedings

Courtroom proceedings generate large amounts of scanned documents that need to be accurately digitized. AI-powered OCR systems can automatically convert scanned files into text, creating accurate and usable transcripts of court proceedings. This will streamline the documentation process and will also enhance the distribution and sharing of digital content. The digital content is easier to handle, sign, and archive.

 

Example of Optical Character Recognition (OCR)
Optical Character Recognition (OCR) for legal scenarios to convert scanned files into text

 

4. Intellectual Property (IP) Protection and Deepfakes

With the ubiquity of smartphones and social media, technology threatens the digital content that can now be vastly plagiarized. Many individuals try to steal the authors’ inventions, which can include text, video, audio, or images.

Additionally, with the rise of generative AI technology (Chat-GPT, DALL-E), deepfakes have become a technological burden that can create illicit content with the same meaning, but a different look from the original.

 

Examples of different facial manipulation used in deepfakes.
In visual media, deepfake tools employ several methods to manipulate different characteristics or features.

 

To overcome this IP concern – researchers have applied a Convolutional Neural Network (CNN) to detect plagiarized text and images as well as problematic deepfakes on the internet. X. Liu, et. al., (2020) applied an image copy detection scheme based on the deep learning Inception CNN model. Their image dataset was transferred by several image processing manipulations. The image’s feature values were automatically extracted for learning and detecting unauthorized digital images. By applying rotation, scaling, and other content manipulations they obtained satisfying results in the process of detecting duplicated images. Further probing with the CNN model with multiple combinations of original and manipulated images will improve the accuracy of image copy detection.

 

5. Forensic Analysis

Crime investigation depends on the collection and analysis of different types of evidence. Impression evidence, such as footprints, tire tracks, and side marks are important sources of physical evidence for law enforcement agencies. They can help confirm or reject information provided by witnesses or suspects. According to a study conducted in Switzerland, shoe prints can be found in around 35% of all crime scenes.

 

Footprint detection and recognition with computer vision for law and legal applications
Footprint recognition – source.

 

In 2012, researchers applied multi-view, stereo techniques to obtain a three-dimensional model from photographs taken from a footwear impression, commonly found in crime scenes. In their approach, the goal was to reconstruct shredded images. Their pipeline included photogrammetry and 3D reconstruction of the impressions, which together produced a complete 3D model from a collection of photographs taken at different angles around the impression.

 

6. Face Recognition in Legal Investigations

Perhaps the most applicable computer vision technology in law cases is facial recognition. In law enforcement, facial recognition is useful for surveillance, alarm, and crime prevention, and several companies are in the run to develop the best artificial intelligence software to make facial recognition better.

AI emotion recognition can help detect when a person may have been involved in a crime either as a perpetrator or an eyewitness. The face recognition system should be able to detect certain expressions on people’s faces indicating guilt or if a person is keeping secret information of interest for the crime committed. CV methods and tools have proven to be valuable in solving crimes or detecting possible criminal activities.

 

Face Detection using Computer Vision with Facial Keypoints
Face Detection using Computer Vision with Facial Keypoints

 

There are image-based and feature-based methods for face detection. Feature-based methods try to find invariant features of faces for detection, while image-based methods try to learn templates from examples in images. Datasets for face recognition include:

  • PASCAL FACE – contains 1335 labeled faces in 851 images with large face appearance and pose variations
  • AWF (Annotated Faces in Wild dataset) – includes 205 images with 473 labeled faces.
  • MIT – CBCL dataset, contains a training set (2429 faces, 4548 non-faces) and a test set (472 faces, 23573 non-faces).

 

7. Virtual Courtrooms

Virtual environments are commonplace in multiple court situations. They can facilitate trial preparation, present evidence and support arguments during trials, question remote witnesses, and provide a recording of the trials. This technology is useful mainly for attorneys delivering presentations to the judge (or jury) or eliciting reactions and opinions from experts, either in preparation for or at trial. VR can expose the situation to the jury from the position of the parties and witnesses to the crime events.

 

Courtroom application with computer vision for virtual reality
Virtual Courtroom application: 1) position tracking camera, 2) orientation sensors, 3) image generation – source.

 

On the other hand, videoconference is useful to provide testimony and cross-examination of remote witnesses and expert witnesses during legal processes. The people who are not able to attend the trial can still provide valuable testimony by using VC/VR technology. Currently, researchers are exploring the possibilities of using virtual environments as a substitute for videoconferencing.

 

8. Compliance Monitoring

The construction industry is one of the most dangerous sectors concerning occupational safety because of the dynamic and messy environment at construction sites. Safety and compliance are complementary to each other and are crucial for managing the safe conditions at construction. Computer Vision techniques in civil engineering can be a key component for improved monitoring in terms of safety and compliance conditions. To fulfill safety and compliance requirements in construction – some researchers have applied advanced CV and deep learning methods.

M. Nain and his team (2020) examined various CV frameworks along with deep learning, to achieve efficient and cost-effective safety (compliance monitoring). They utilized image classification and detection by SVM (Support Vector Machines). Also, they extracted features automatically with deep learning using CNNs and RNNs (Recurrent Neural Networks). They concluded that the best algorithms for real-time issue detection are YOLO, SSD, RetinaNet, and R-FCN.

 

CNN for compliance monitoring in the construction industry
Deep Learning with CNN in Compliance monitoring for the construction industry – source.

 

9. Evidence Authentication

An important task in the law (legal) systems is the evidence analysis. Classical methods of reviewing evidence, such as images, videos, and legal documents, are time-consuming and error-prone. AI systems can quickly and accurately analyze evidence, marking relevant details and finding mistakes that might go unnoticed by legal professionals or human observers. In addition, CV can help in the authentication of evidence by detecting unauthorized changes or manipulations, thus guaranteeing the integrity of the judicial process.

 

Applying Computer Vision for Legal Purposes

Legal tech must integrate computer vision and artificial intelligence in law applications. By offloading menial, time-heavy, cost-burdensome tasks by allowing computers to “see,” the legal industry will gain the ability to quickly adapt and succeed with the ever-evolving legal landscape. To learn more about computer vision applications, check out these other articles on our blog:

Viso Suite gives enterprises the ability to easily solve complex business challenges without a single line of code. Find out more about computer vision solutions in the world of law and regulation by booking a demo with us.

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