An Automated Hierarchical Framework for Player Recognition in Sports Image

The recognition of player in the soccer game is a challenging task due to the continuous variation in the position, view, orientation and distance of the players as well as the camera. In order to resolve this problem, the player's identification number (which is printed on their jersey) is recognized and based on the number, players are identified. This paper proposes a noble Optical Character Recognition (OCR) system that automatically extracts and recognizes the player's jersey number in the image. A hierarchical computational framework is introduced which uses high and low-level vision features of the image to identify the player. While following the hierarchical approach a parsing tree is constructed by segmenting the image into snippets based on different algorithms. The entire parsing tree is divided into 4 levels: Level 1 deals with the playfield detection and shot classification, Level 2 tackles the player localization and extraction, Level 3 covers the player's number extraction and shape restoration, and Level 4 recognizes the player's number. The proposed OCR system has consistently shown outstanding results in terms of efficiency, effectiveness, and robustness over a large soccer dataset.