本文選題：文字識(shí)別 + 圖像處理��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：為了方便提高鐵路貨運(yùn)管理的工作效率,減少企業(yè)對(duì)貨運(yùn)列車管理的投入。本文中利用文字識(shí)別相關(guān)技術(shù)對(duì)貨運(yùn)站點(diǎn)的火車車廂信息進(jìn)行抓拍識(shí)別,并對(duì)識(shí)別記錄進(jìn)行存儲(chǔ)管理。該方式改變了以往軌道衡值班人員在戶外條件下對(duì)車廂載重、自重和容積等屬性信息進(jìn)行人工的記錄并手動(dòng)錄入計(jì)算機(jī)存儲(chǔ)的現(xiàn)狀。同時(shí)減少了人工作業(yè)記錄車廂信息出現(xiàn)的誤差。本文中實(shí)現(xiàn)的系統(tǒng)可以有效地記錄并管理車廂信息,大大降低人為因素的干預(yù),同時(shí)減輕了軌道衡值班人員的工作量,節(jié)省企業(yè)對(duì)此項(xiàng)工作的人力投入。本文中利用網(wǎng)絡(luò)攝像機(jī)、光電傳感器等設(shè)備實(shí)現(xiàn)了一套完整的針對(duì)貨運(yùn)列車信息的識(shí)別系統(tǒng)。通過利用貨運(yùn)列車行進(jìn)過程中車廂間隙的特征,結(jié)合一對(duì)光電光感器研究實(shí)現(xiàn)了一種針對(duì)貨車車廂文字的控制抓拍方法。利用現(xiàn)場架設(shè)的多部攝像機(jī)對(duì)車廂兩側(cè)的文字信息進(jìn)行抓拍,通過將車廂兩側(cè)不同質(zhì)量的文字圖像識(shí)別結(jié)果進(jìn)行對(duì)比,以此來提高文字識(shí)別效率,其中識(shí)別的結(jié)果包括火車車型、車廂號(hào)、載重、自重、容積、寬高、換長等信息。系統(tǒng)同時(shí)利用射頻識(shí)別(Radio Frequency Identification,RFID)通信技術(shù)對(duì)識(shí)別結(jié)果進(jìn)行補(bǔ)充完善。結(jié)合現(xiàn)有成熟的視頻監(jiān)控手段,在傳感器或識(shí)別功能出現(xiàn)故障時(shí)對(duì)過衡時(shí)的錄像進(jìn)行慢鏡頭回放,由值班人員根據(jù)系統(tǒng)錄像回放補(bǔ)充車號(hào)、載重等信息。在文字識(shí)別部分中,對(duì)鐵路貨運(yùn)列車車廂文字信息的識(shí)別進(jìn)行研究,由于該應(yīng)用場景的文字具有筆畫不連續(xù)、筆畫間隔大且受環(huán)境因素腐蝕嚴(yán)重等特點(diǎn),利用傳統(tǒng)的模版匹配或幾何特征抽取等方法不能達(dá)到很好的識(shí)別效果,本文是選擇卷積神經(jīng)網(wǎng)絡(luò)(Convolutional Neural Networks,CNN),通過前期圖像分割得到大量數(shù)據(jù)樣本進(jìn)行訓(xùn)練識(shí)別。其中圖像分割處理流程則是對(duì)原始圖像利用以鈴木算法為核心進(jìn)行輪廓提取后確定文字區(qū)域。在根據(jù)文字區(qū)域的邊緣信息水平投影,結(jié)合文字固定的寬高比例得到遍歷模版后完成單個(gè)文字圖像分割。通過系統(tǒng)現(xiàn)場實(shí)際測試得出,本文中設(shè)計(jì)的系統(tǒng)及采用的識(shí)別方法可以快速準(zhǔn)確的識(shí)別指定場景中的文字信息,貨車信息識(shí)別效果能達(dá)到應(yīng)用標(biāo)準(zhǔn)。
[Abstract]:In order to improve the efficiency of railway freight management and reduce the investment of freight train management. In this paper, the relevant technology of character recognition is used to capture the train compartment information of freight station, and to store and manage the identification record. This method changed the situation of manual recording and manual storage of the load, weight and volume of the carriage under outdoor conditions. At the same time, the error of recording carriage information by manual operation is reduced. The system realized in this paper can effectively record and manage the information of the carriage, greatly reduce the intervention of human factors, at the same time reduce the workload of the personnel on duty of the track scale, and save the manpower input of the enterprise in this work. In this paper, a complete identification system for freight train information is realized by using network camera, photoelectric sensor and other equipment. Based on the characteristics of the gap between the freight trains and a pair of optoelectronic light sensors, a method of controlling and capturing the characters of the freight cars is presented in this paper. The text information on both sides of the car was captured by using a number of cameras set up on the spot, and the result of text image recognition of different quality on both sides of the car was compared to improve the efficiency of character recognition. The results include train model, car number, load, weight, volume, width and length. At the same time, the system uses radio frequency identification / radio frequency identification (RFID) communication technology to supplement and improve the identification results. Combined with the existing mature means of video surveillance, the slow motion video when the sensor or recognition function fails is played back in slow motion, and the personnel on duty play back the information of vehicle number and load according to the video recording of the system. In the part of character recognition, the text information recognition of railway freight train carriage is studied. The characters of the application scene are characterized by discontinuous strokes, large stroke intervals and serious corrosion by environmental factors. Traditional methods such as template matching or geometric feature extraction can not achieve a good recognition effect. In this paper, Convolutional Neural Networks CNNs are selected and a large number of data samples are obtained by image segmentation for training and recognition. The process of image segmentation is to use Suzuki algorithm as the core to extract the contour of the original image and determine the text area. Based on the horizontal projection of the edge information of the text region and the fixed width and height ratio of the text, the traversal template is obtained, and the segmentation of a single text image is completed. Through the field test of the system, it is concluded that the system designed in this paper and the recognition method used in this paper can quickly and accurately recognize the text information in the specified scene, and the recognition effect of the truck information can reach the application standard.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U29-39;TP391.41

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