Gear defects are major reason for poor quality and embarrassment for manufactures. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying gear defects require more automotive and accurate inspection process. Considering this lacking, this search implements a gear defect recognizer which uses computer vision methodology with the combination of local threshold to identify possible defects. The recognizer identifies the gear defects within economical cost and produces less error prone inspection system in real time. In order to generate data set, primarily the recognizer captures digital gear images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later the outputs of the processed image are the area of the faulty portion and compute the possible defective and non-defective gear as an output. Keywords— defect detection, image processing, computer vision.