1.算法簡介

? ? ? OCR識別可以分為數字掃描對象的獲取,數字圖像的生產,數字圖像的處理和OCR文本識別等四個階段.OCR識別在信息資源數字化工作中應用時,其準確度一直是人們關注的焦點,因為OCR精確識別是保證數字化產品質量進而為整項工作提供用戶保障的一個重要環節.?

2.部分核心代碼

% OCR (Optical Character Recognition).% PRINCIPAL PROGRAM warning off %#ok<WNOFF> % Clear all clc, close all, clear all % Read image imagen=imread('TEST_1.jpg'); % Show image imshow(imagen); title('INPUT IMAGE WITH NOISE') % Convert to gray scale if size(imagen,3)==3 %RGB imageimagen=rgb2gray(imagen); end % Convert to BW threshold = graythresh(imagen); imagen =~im2bw(imagen,threshold); % Remove all object containing fewer than 30 pixels imagen = bwareaopen(imagen,30); %Storage matrix word from image word=[ ]; re=imagen; %Opens text.txt as file for write fid = fopen('text.txt', 'wt'); % Load templates load templates global templates % Compute the number of letters in template file num_letras=size(templates,2); while 1%Fcn 'lines' separate lines in text[fl re]=lines(re);imgn=fl;%Uncomment line below to see lines one by one%imshow(fl);pause(0.5) %----------------------------------------------------------------- % Label and count connected components[L Ne] = bwlabel(imgn); for n=1:Ne[r,c] = find(L==n);% Extract lettern1=imgn(min(r):max(r),min(c):max(c)); % Resize letter (same size of template)img_r=imresize(n1,[42 24]);%Uncomment line below to see letters one by one%imshow(img_r);pause(0.5)%-------------------------------------------------------------------% Call fcn to convert image to textletter=read_letter(img_r,num_letras);% Letter concatenationword=[word letter];end%fprintf(fid,'%s\n',lower(word));%Write 'word' in text file (lower)fprintf(fid,'%s\n',word);%Write 'word' in text file (upper)% Clear 'word' variableword=[ ];%*When the sentences finish, breaks the loopif isempty(re) %See variable 're' in Fcn 'lines'breakend end fclose(fid); %Open 'text.txt' file winopen('text.txt') fprintf('For more information, visit: <a href= "http://www.matpic.com">www.matpic.com </a> \n') % clear all

3.仿真演示

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4.相關參考文獻

[1]郭軍. 信息資源數字化文本型數字圖像OCR識別準確度影響因素及提高策略研究[D]. 鄭州大學.

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