Research On Detection And Recognition Of Label Images Of Archive Boxes In Revolving Warehouses

As the most original first-hand information,archives have extremely important preservation,which are usually bound into volumes and put into boxes for safekeeping.There are a large number and high density of file boxes that exist problems of low efficiency and high labor costs in its daily inventory.With the continuous development of modern information technology,the management of archives tends to be increasingly digital.Thus,how to utilize modern information technology to realize the automatic detection and identification of archival boxes to achieve efficient archival management is a problem that many scientific and technical workers have been devoted to addressing.Given the storage characteristics of a rotating storage cabinet with relatively high space utilization,this paper studies the detection technology based on the image of the file box label and identifies and compares the file box label through OCR,image matching and other digital image processing technology,which can greatly improve the efficiency of the inventory of the file box.The main work and innovation of this paper are as follows:(1)The advantages and disadvantages of traditional image segmentation algorithms and deep learning segmentation algorithms are analyzed and compared.Traditional digital image processing methods use classical edge detection segmentation algorithms and threshold-based segmentation algorithm.Deep learning segmentation algorithm uses a U-net neural network with excellent segmentation performance.The experimental results show that the traditional segmentation algorithm is greatly affected by noise,and plenty of uncertain parameters need to be adjusted according to the application scene,which can not be fully adaptive;while using the U-net neural network to segment the file box label image is much better since there is no need to adjust any parameters and has the strong anti-interference ability and good segmentation effect.(2)It mainly focuses on the detection and recognition method of file box labels by using Tesseract-OCR character recognition technology.To realize the character recognition of the "vertical row" of the file box label,it studies the use of the projection method to segment the individual text in the label,and discusses the use of image morphology processing method to enhance the segmentation effect of the label.For improving the accuracy of character recognition,the font database data of the file box label is enhanced,and the training results can better enhance the character recognition effect.(3)This paper studies the file box label detection technology of the SURF algorithm for image matching and tests the stability of the SURF algorithm through various interferences.In the light of the file box label image in the actual environment corresponding image enhancement processing,including automatic color equalization algorithm,denoising algorithm,promote the actual environment of the file box label image matching accuracy.(4)Aiming to further improve the matching accuracy of the SURF matching algorithm,the SURF matching algorithm based on HSV color extraction,the SURF matching algorithm based on RANSAC algorithm,and the SURF matching algorithm based on two-way matching and distance optimization were proposed according to the characteristics of the color of the label images and placement of the box of the rotating library.These improved SURF algorithms all upgrade the matching accuracy to a certain extent.