Enhancing an Astrology App's Palm Reading Accuracy by 25% with Precise Image Annotation Services

The client approached us with a critical task to provide accurately labeled palm images for training their AI algorithm. The specific requirements were:

• A large dataset containing a total of 10,000 palm images were to be labeled, for AI model training.
• Precisely identify and categorize various regions of the hand, such as finger points (5 regions), the area below the fingers (5 regions), and hand mounts (9 regions per palm).
• Ensure high-quality, consistent labeling across a diverse set of hand images to maximize the AI's ability to identify these critical regions when processing user images.
• Deliver labeled images in a format compatible with the client's AI training pipeline.

The project involved significant challenges that required our team to develop custom solutions and refine our processes including:

• Diverse Images

  The palm images received by the client varied significantly in terms of postures and positions, making it difficult to identify key points like hand mounts. Additionally, variations in lighting, hand angles, and finger positioning further complicate the annotation process.

• Unrecognized Palm Areas

  While the core focus was on labeling key regions like finger points and hand mounts, there were additional unrecognized areas, such as gaps between fingers which had to be carefully categorized by annotators.

• Image Clarity Issues

  Several client-provided images were unclear which affected the ability to recognize the palm lines. This was mainly due to poor lighting, darker skin tones, or shadows.

• AI Tool Limitation

  The annotation tool was insufficient for accurately detecting and labeling certain regions, such as the gaps between fingers or the edges of the palm. Manual intervention was necessary to accurately label these regions.