Large-area electronics (LAE) enables the formation of a large number of sensors capable of spanning dimensions on the order of square meters. An example is X-ray imagers, which have been scaling both in dimension and number of sensors, today reaching millions of pixels. However, processing of the sensor data requires interfacing thousands of signals to CMOS ICs, because implementation of complex functions in LAE has proven unviable due to the low electrical performance and inherent variability of the active devices available, namely amorphous silicon (a-Si) thin-film transistors (TFTs) on glass. Envisioning applications that perform sensing on even greater scales, this work presents an approach whereby high-quality image detection is performed directly in the LAE domain using TFTs. The high variability and number of process defects affecting both the TFTs and sensors are overcome using a machine-learning algorithm known as Adaptive Boosting (AdaBoost)  to form an embedded classifier. Through AdaBoost, we show that high-dimensional sensor data can be reduced to a small number of weak-classifier decisions, which can then be combined in the CMOS domain to generate a strong-classifier decision.