A novel skew correction methodology for handwritten words in multilingual multi-oriented documents

Pramanik, Rahul; Bag, Soumen

doi:10.1007/s11042-021-10822-2

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…The disadvantage is that the accuracy is low when the characters are not strictly aligned. Pramanik et al [8] corrected the inclination of the text by extracting the features of the title in the document image and using a linear curve fitting detection method. The inclination angle between plus and minus 55° can achieve a calibration rate of 86%, but this method only works for document images and not for industrial character images.…”

Section: Introductionmentioning

confidence: 99%

Research on the Recognition Method of the Axle End Mark of a Train Wheelset Based on Machine Vision

Liu

Zeng

et al. 2022

Int J Comput Intell Syst

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Whether the wheelset of a high-speed train has defects such as cracks is very important to the safety of high-speed trains. Hence, the wheelset must be regularly inspected for flaws. For flaw detection of a wheelset, it is necessary to record the axle end information of the wheelset to correlate with the flaw detection results. To quickly and accurately identify the axle end mark of the wheelset, an automatic identification method based on machine vision is proposed. Our method identifies seven types of marks on the axle end, including the smelting number, steel grade number, unit number, sequence number, year and month, axle type mark, and the azimuth mark. Using the established automatic identification method of axle end marks, based on Retinex theory, an improved dual-core Laplacian combined with Gaussian filtering operation is proposed to solve the problem of the low contrast of the wheelset axle end image. An improved image tilt correction algorithm based on the combination of Hough circle detection and bilinear interpolation is proposed, which solves the angle tilt problem of the target character area of the axis end image. To handle the various types of axis end markers and the small amount of data, a retraining method to improve recognition accuracy is proposed. This method first uses Chi_Sim as the basic font for training and then retrains based on the trained font. Finally, Tesseract-OCR is used to improve the accuracy of the recognition results. Experiments are carried out by developing an automatic recognition program for axle end marks. The results show that the proposed method can effectively identify and classify seven-character types, and the recognition accuracy reaches 96.88% while the recognition time of each image is 5.88 s.

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Section: Introductionmentioning

confidence: 99%