Challenge

Creating a smooth and efficient onboarding process in e-commerce presents a significant hurdle. To overcome this challenge, implementing an advanced search feature is crucial. This feature needs to enable both sellers and buyers to quickly find products by scanning images or videos. The task is to develop and integrate this innovative functionality seamlessly into existing platforms, streamlining the onboarding process and enhancing user experience.

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Solution

Our strategy involves utilizing deep learning models like Convolutional Neural Networks (CNNs) to address the challenge. We will be collecting a wide dataset of product images, and then we will be training a CNN model to recognize the products; we will periodically update it with new data, and by using a feedback mechanism, we will enhance the model over time.

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Data Collection

Users can capture images and videos of products. These videos are converted into images and are analyzed by advanced deep learning models

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Model Training

Unlabeled, raw images are processed by ML model to identify the objects in them. Images that contain textual labels are sent through the OCR model to detect and extract the words on the product. The extracted labels from the images are then processed by a custom Python algorithm designed to distill keywords from the labels.

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Continuous Improvement

For products that are not identified, users will be provided with options to recognize them using the data available in the repository. Subsequently, the model can undergo retraining through the transfer learning approach, utilizing the TensorFlow model for image classification. Transfer learning allows us to preserve previously acquired knowledge while incorporating the newly added dataset into the training process.