In the process of steel billets recognition on the production line, the key problem is how to determine the position of the billet from complex scenes. To solve this problem, this paper presents a positioning algorithm based on the feature variance of billet character. Using the largest intra-cluster variance recursive method based on multilevel filtering, the billet characters are segmented completely from the complex scenes. There are three rows of characters on each steel billet, we are able to determine whether the connected regions, which satisfy the condition of the feature variance, are on a straight line. Then we can accurately locate the steel billet. The experimental results demonstrated that the proposed method in this paper is competitive to other methods in positioning the characters and it also reduce the running time. The algorithm can provide a better basis for the character recognition.
Slant correction for billet characters is primary and critical step of the recognition of steel billet characters. Character positioning is not accurate when the billet characters are inclined. To solve this problem, this paper presents an algorithm of slant correction for billet characters using height feature of characters. Characters are linearly arranged, using this feature, the angle between the horizontal direction and the base line can be calculated, then the sloping billet characters can be corrected. Experimental results show that the proposed method can correct sloping characters accurately. Compared with the traditional algorithm of slant correction for billet characters, the proposed method can obtain better results.
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