#3770 Σ0ρλδ!!

This week was used in understanding the ANPR algorithm and its different components and steps. There are seven primary algorithms that the software requires for identifying a license plate: Plate localization – responsible for finding and isolating the plate on the picture. Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size. Normalization – adjusts the brightness and contrast of the image. Character segmentation – finds the individual characters on the plates. Optical character recognition. Syntactical/Geometrical analysis – check characters and positions against country-specific rules. The averaging of the recognised value over multiple fields/images to produce a more reliable or confident result. Especially since any single image may contain a reflected light flare, be partially obscured or other temporary effect.

This week the images collected were used to train ANN. Some jugaad   was also employed to overcome difficulties and Ouroboro effect was also used to create normalized segmented images of characters.  It could be the Achilles' heel of the main application. But still it might be workable. 

Most of the week was spent in gathering the images to train SVM, but still I was unable to gather the required number of images. It was quite an interesting week in terms of facing from angry owners to disgruntled guards. Finally I've decided to use the older XML file as it works pretty well with the given specifications. 

This week was devoted in learning how to extract lines, contours and components of images. Day 46: Detecting Image Contours with the Canny Operator Day 47:  Detecting Lines in Images with the Hough Transform Day 48: Fitting a Line to a Set of Points Day 49:  Extracting the Components & Contours Day 50: Computing Components & Shape Descriptors