Performance evaluation of GANs in a semisupervised OCR use case

Description

Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically.

Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data.

What you'll learn:

• Understand how semisupervised learning with GANs works
• Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data
• Gain insight into an interesting OCR use case of an online vehicle marketplace

Speaker: Dr. Florian Wilhelm

Event: O'Reilly Artificial Intelligence Conference, London und PyCon.DE

Datum: 11.10.2018 (AI Conf) und 24.10.2018 (PyCon)

Dr. Florian Wilhelm ist Data Scientist bei inovex. Er verfügt über mehrjährige Projekterfahrung im Bereich Predictive & Prescriptive Analytics und Big Data und über fundierte Kenntnisse in den Bereichen mathematische Modellierung, Statistik, maschinelles Lernen, Hochleistungsrechnen und Data Mining.