RESEARCH Open Access

Translation analysis of English addressimage recognition based on imagerecognitionShengfang Wen

Abstract

In the current English semantic recognition of mail, there are problems such as serious information distortion andunrecognizable, which affects the promotion of machine automation to recognize text in emails. This studycombines the actual situation of the mail image to set the corresponding image processing algorithm, adopts theconversion from RGB data to gray data to perform image gray processing, and combines the weighted averagegray algorithm to improve the image gray definition and softness. At the same time, this study uses homomorphicfiltering to enhance the image, uses homomorphic filtering to enhance the sharpness of the text, and uses medianfiltering to enhance the image. Image edge recognition is performed with the Sobel color difference operator, andthe text result is output. Combining experiments to study the performance of the algorithm, the research showsthat the algorithm has certain practical effects, which can provide reference for subsequent related research.

Keywords: Image recognition, English, Text, Translation, Semantics

1 IntroductionAt present, English has become the most popular lan-guage in the world. Most of the files in the world arestored in English. In the era of intelligent information, filerecognition is mostly carried out through intelligent rec-ognition, which shows that the semantic recognition ofEnglish images is very important. English mail is mostlycompleted by handwriting. How to realize automatictranslation of English address has become an importanttopic of postal automation. Mature character recognitiontechnology is the key to processing mail information.Therefore, this study analyzes English address image rec-ognition based on image processing technology.The text detection technology of complex background,

as a kind of target detection, reached a research peak inthe last 5 years. Unlike general target detection tasks,text detection in natural scenes is more challenging. Sofar, this technology has not been completely solved bypeople, and the application of text detection algorithmsto practical systems still needs to be continued [1]. Textdetection of complex backgrounds is quite differentfrom traditional text detection. It is these differences

that add to the difficulties and challenges of this re-search. First of all, traditional text detection is mainly forstandard documents or text with specific positions, andpeople can make full use of the position and shape char-acteristics of the text to extract them. However, text innatural scenes is more versatile, and text can appearanywhere in the image. In the early days of development,the complexity of machine translation was underesti-mated, and machine translation was only limited to theconversion between word layers. Many machine transla-tion research scholars compare the process of machinetranslation with the process of interpreting passwords.They try to realize the machine translation ofword-to-word by querying the dictionary. Therefore, thereadability of the translation is poor and it is difficult toput it into practice [2]. In 1966, the black paper“Language and Machine” published by the NationalAcademy of Sciences reported a negative attitude to-wards machine translation. They believe that machinetranslation research has encountered insurmountable“semantic barriers,” and it is impossible to develop atruly practical machine translation system in the shortterm. This report has caused many countries in theworld to stop supporting machine translation research,Correspondence: 8968@whxy.edu.cn

Wuhan College, Wuhan 430212, China

EURASIP Journal on Imageand Video Processing

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Wen EURASIP Journal on Image and Video Processing (2019) 2019:11 https://doi.org/10.1186/s13640-019-0408-9

and many established machine translation research unitshave encountered administrative and financial difficul-ties. Therefore, the study of machine translation in the1960s once entered a low tide [3]. Until the early 1970s,machine translation was re-developed based onreal-world needs and advances in technology.Researchers in machine translation are generally awarethat the differences between the original language andthe translated language are not only expressed in the dif-ference of vocabulary, but also in the difference in syn-tactic structure. Therefore, in order to obtain a readabletranslation, more efforts must be made in automatic syn-tactic analysis [4]. The USA, Japan, Canada, France, andthe Soviet Union have successively established a series ofmachine translation systems. In 1976, the University ofMontreal in Canada and the Canadian Federal Govern-ment Translation Agency jointly developed the practicalmachine translation system TAUM-METEO to provideweather forecasting services. The system can translate60,000–300,000 words per hour and can translate 2000weather forecasts per day. The METEO system is a mile-stone in the history of machine translation development,and a series of practical and commercialized machinetranslation systems have emerged [5]. The ATLAS-I sys-tem developed by Fujitsu of Japan is an English-Japanesemachine translation system built on a large computer.The system is centered on syntactic analysis and can beused to translate scientific and technical articles [6]. TheFrench Textile Research Institute’s TITUS-IV system canbe translated into English, German, French, and Spanish.The system is mainly used to translate textile technologyliterature [7]. The system provided by the USA to theUS Air Force can be used for Russian-English machinetranslation and the translation system provided by theUSA to the American Ratsek Company can be translatedinto Russian-English, English-Russian, German-English,Chinese-French, and Chinese-English [8].At the current level of image processing, some

scholars are already trying to translate signboards andtexts taken by photographs or videos. The CarnegieMellon University’s Sign Translation Interplay SystemLab is mainly engaged in the research of text and variousautomatic extraction techniques, applies their researchresults to the TIDES project, and has developed aChinese-English translation software based on the PDAplatform [9]. Kapsouras et al. also studied image transla-tion in English and Spanish and implemented it on a PCusing MATLAB [10]. In his mobile phone camera-basedreal-time translation software, Herekar et al. used aug-mented reality technology to restore the backgroundand superimpose the translation results [11]. The mainproblem with the current research is that there are mostof the target areas that need to manually select the textto be translated. In addition, many studies are aimed at

images such as street signs and photographs taken byhandheld devices. The translation attempts of inline im-ages in web pages are still in the research stage and havenot been popularized in the commercial world. However,as far as the progress has been made, text image analysiswill eventually go from the laboratory to the market andbe applied to all aspects of people’s life and work [12].According to the above analysis, some advanced tech-

nologies in the semantic recognition of image texts havebeen applied, but there are still some problems in the se-mantic recognition of emails. Based on this, this study takesEnglish mail as an example to carry out English image rec-ognition translation research and proposes correspondingresearch methods to promote the application of image rec-ognition technology in mail semantic recognition.

2 Research methods2.1 Grayscale processingFor address image recognition, it is first necessary to ex-tract the image frame by video or photographing as animage detection technique. The grayscale process dis-cussed in this study refers to the conversion of imagesfrom RGB data to grayscale data. The RGB data formathas advantages that other data formats do not have. Thedata format has clear and clear physical representationand is based on the principle of three primary colors.Therefore, most of the color images are acquired,accessed, and displayed using the RGB data format.However, the RGB data format itself does not distinguishthe blackness and brightness information of the image,so in some specific applications such as image analysisand some specific color-based recognition, the RGB for-mat will bring a lot of complexity. This paper uses theweighted average gray scale algorithm [13].Weighted average method: according to specific needs,

different weight components are given different weightsaccording to importance, and the weight value is obtainedas the gray value of the point, which can be expressed as:

R ¼ G ¼ B ¼ KrRþ KgG þ KbB ð1Þ

Among them, Kr, Kg, and Kb are the weighting valuescorresponding to the R, G, and B components, respect-ively. According to different needs, different weightvalues are assigned to the respective color components,and the obtained grayscale image will have a consider-able difference. In terms of human vision, the humaneye is most sensitive to the green component, red isslightly second, and blue sensitivity is the lowest, so gen-erally, Kr > Kr > Kb. The experimental theory shows thatwhen Kr = 0.3, Kg = 0.59 and Kb = 0.11, the obtained gray-scale image is relatively reasonable. The image obtainedby the algorithm is more in line with human vision, and

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the brightness is relatively moderate, which is convenientfor human eye recognition and machine processing. Asshown in Fig. 1, the grayscale processing effects of the max-imum value method, the average value method, and theweighted average method are respectively compared. Asshown in Fig. 1a, b, and d show the effect of grayscale pro-cessing by the maximum value method, the average valuemethod, and the weighted average method, respectively.It can be seen that among the three gray-scale algo-

rithms, the image obtained by the maximum value algo-rithm has the lowest brightness, the image of theaverage value algorithm has low brightness, and theimage resolution is poor. However, compared with themaximum method, the weighted average method has nosignificant difference in image resolution, but the imagebrightness is relatively moderate.

2.2 Image enhancementThe image enhancement of the image is mainly to makethe image part of interest of the image clearer by imageprocessing, thereby facilitating subsequent image pro-cessing. For specific problems, different image compo-nents need to be enhanced, and the ultimate goal is tomake the image more suitable for human visual charac-teristics or machine reading characteristics. Image en-hancement cannot increase the amount of information,but simply amplifies a certain type of information ofinterest. At the cost, many irrelevant and weakly relatedinformation is ignored. Image enhancement techniquesmainly include arithmetic, logic processing enhance-ment, space, frequency domain filtering enhancement,histogram conversion processing, and mixed space en-hancement. When performing image enhancement, notonly an enhancement technique can be applied, but alsomultiple enhancement techniques can be selected for abetter processing effect. There are two main methods ofimage enhancement: image enhancement in the spatialdomain and image enhancement processing in the fre-quency domain. The spatial domain enhancement tech-nique mainly deals with image bitmap pixels as theprocessing target, while the frequency domain method

achieves the goal by performing Fourier transform onthe image pixel stream [14].Since the text content on the image text is very much,

the information in the detail part is also very important.Therefore, this paper adopts the homomorphic filteringmethod for image enhancement. At the same time,through homomorphic filtering, it is also possible tohighlight the detailed information of the dark portion ofthe image due to uneven illumination, which is benefi-cial to the next image processing. The processing stepsof homomorphic filtering are as follows: (1) the inputimage S (x, y) is subjected to digital processing to obtain L(x, y). (2) L (x, y) is carried out a fast Fourier transform. (3)The obtained image is subjected to frequency domain fil-tering. (4) The image is subjected to inverse fast Fouriertransform. (5) The image obtained in the previous step issubjected to exponential operation to obtain the finalhomomorphic filtered output image [15].The processing time of homomorphic filtering is rela-

tively longer than other enhancement algorithms, butthere is no obvious increase. Therefore, in view of thegood guarantee of image outline information, this paperchooses to sacrifice a certain processing time to achievebetter enhancement. At the same time, this paperchooses to make reasonable improvements in the subse-quent algorithms to reduce the processing time of thewhole algorithm, so as to achieve the real-time require-ments of the whole system design [16].

2.3 Image denoising processingThe noise in digital images mainly comes from the ana-log transmission process of image information and theprocess of digital channel propagation and image acqui-sition. The channel is affected by incomplete control-lable factors such as temperature and humidity in theactual environment and will exhibit unavoidable volatil-ity. This volatility will be introduced into the transmittedsignal and injected into the image data as noise informa-tion. It will affect the subsequent image processing, so itmust be reduced by certain measures.

Fig. 1 Comparison of grayscale processing effects

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In this paper, the method of median filtering isadopted, which is easy to implement, the image process-ing effect is quite good, and the algorithm is faster, sothe recognition time can be saved to some extent. Unlikemean filtering, median filtering is a nonlinear filteringtechnique that is based on sequencing theory and can ef-fectively suppress noise signals. The main implementa-tion of median filtering is as follows: first, for any pixel,the neighboring points are sorted and searched accord-ing to the gray value, and then the intermediate result ofthe sorting result is selected as the gray value of thepoint. Performing this operation on all pixels will resultin a median filtered image. In general, the gray levels ofadjacent dots in the image are continuous, and there islittle possibility of a sudden change. For those pointswhere the grayscale value of the pixel is significantly dif-ferent from its neighborhood point, we change its pixelgray value to the median of its neighborhood point. Thesteps of implementing the image noise reduction algo-rithm in this study are as follows: (1) the neighborhoodof the pixel is selected, which is generally a squareneighborhood. In this paper, a 5 × 5 neighborhood isused. (2) The gray values of the neighborhood pixels aresorted. (3) The middle value of the ordered gray valuesequence is selected as the 5 × 5 neighborhood selectedby the gray value of the point, which is called a window.The window moves up, down, left, and right on theimage until all image portions are covered, at whichpoint the image filtering is complete. The median filter-ing is faster than the mean filtering, and the filtering ef-fect on complex noise signals is much better. Thewavelet transform has the best filtering effect, but the fil-tering takes the longest time. Because the identificationsystem designed in this paper requires the system tomeet the requirements of real-time and needs to meetthe requirements of speed, we choose the median filterthat can meet the performance and speed requirements.

2.4 Address image location analysisThe letter image usually consists of the address of theaddressee, the area of the stamp, the address of thesender, the postmark, etc. The letter is transmittedthrough a black belt, and the black area around theletter is the black belt area. The letter layout is rela-tively fixed, which is helpful for locating the addressof the addressee. The image of the letter can be re-ferred to Fig. 2, and the acquired images are all grayimages. In order to ensure the accuracy of image ac-quisition, the size of the acquired image is usually2560 × 2048. The angle of the envelope image col-lected by the system is usually within 5°, which doesnot affect the subsequent analysis, so there is no needto consider the problem of tilt correction.The steps to locate the address of the recipient are as

follows:

1. Remove the black belt area around: The image isscanned horizontally from left to right, and theblack pixels of each line are counted. When thesum of the black pixel points divided by thesum of all the pixel points of the line is lessthan a certain threshold, which is obtainedthrough experimental analysis, then the line isthe upper boundary of the envelope. Similarly,the lower, left and right borders of the envelopeare obtained.

2. Looking for text features: The entire envelope areais divided into M ×N number sub-areas, and a gra-dient feature extraction algorithm is used for eachsub-area to locate sub-areas that may contain text.The gradient feature extraction algorithm is as fol-lows: P number of pixels are set on the scanningline of the jth sub-region, and the gray values, inturn, are f j0; f

j1;…; f jp−1. The gradient D

ji of the ith

pixel on the scan line of the jth is defined as:

Fig. 2 Address area location of the sender and receiver

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Dji ¼ f jiþ2 þ f jiþ1− f

ji− f

ji−1 ð2Þ

In order to better observe the frequency of change of

the gradient Dji , it is noted that V j

i is the gradientquantization value of the ith pixel in the scanning line j,and t is a set threshold.

V ji ¼ 0; if Dj

i > 1255; else

�ð3Þ

In this paper, for each sub-area, gradient analysis is per-formed with four scan lines, and the scan line directionsare as follows: 0o, 45 o, 90 o, 135 o, respectively, whichare denoted as Li, i ∈ [1, 4]. When the frequency of changeof the Li gradient quantized value is greater than threetimes, the scan line may pass through the text area. Ifthere are more than one such scan line in the four sweeplines of the sub-area, the sub-area may be considered tocontain text. Then, the address area is located, and the ad-dress area is located by using the connected element label-ing algorithm. For all sub-regions that match the gradientfeature are marked as pixel foreground, the othersub-regions are uniformly labeled as pixel background.The connected meta tag algorithm can be expressed as

follows: (1) the current pixel is judged whether it is a back-ground. If the pixel is the background, the algorithmjumps to (5), if not, the algorithm jumps to (2). (2) Thecurrent pixel is placed in the stack S. The new connectedelement c is initialized with the four vertices of the corre-sponding rectangle so that it is represented as the size ofthe current pixel. (3) If the stack is not empty, then a pixelin the stack is taken out, all its neighbor pixels are found,the neighbors are pushed onto the stack one by one, andthe neighbor pixels are checked one by one whether theyare outside the current rectangle. If the stack is empty, thefour vertices of the rectangle are modified so that thepixels being looked up are included in the rectangle. (4) Apixel is taken out to check if all pixels have been proc-essed. If all has been processed, one pixel is removed andthe algorithm jumps to (5); otherwise, it jumps to (1). (5)According to the recorded rectangular vertices, one con-nected element is marked in the original picture.

3. Display address location results: according to therelatively stable character of the letter text layout, theaddress area of the addressee is generally in themiddle area or the lower left area of the layout, andthe height, width, and aspect ratio of the address areaof the addressee are maintained within a certainrange. As shown in Fig. 2, the yellow roundedrectangular area is the address area of the addressee,and the blue rectangular area is the other text area.

2.5 Text edge recognitionIn the digital image edge detection technology, noise hasa great influence on the edge detection effect, so theimage is filtered and denoised before the edge detection.Traditional edge detection methods use Gaussian filter-ing, median filtering, mean filtering, and Wiener filteringto denoise. However, these filtering methods obscure in-formation such as edge details of the image. Therefore,it is necessary to find a filtering method that balancesthe noise removal and the remaining edges.The reason why the algorithm uses the vector total

variation minimization model to filter out the noise inthe color image is that the filtering method is faster andcan preserve the edge of the image while filtering outnoise. Meanwhile, the filtering performance is superiorto other color image filtering methods, such as vectormedian filtering and basic vector direction filtering.These filtering methods have a long denoising time andblur the edges of the image The Canny edge detectionmethod is to calculate the gradient magnitude andmethod of the image. The gradient of the image calcu-lated in the color RGB space is more complicated. Thegradient amplitude does not necessarily reflect the hu-man eye’s perception of the local color difference. How-ever, the color difference of the CIELAB space canreflect the human eye’s perception of color differences.Because the CIELAB color space is a uniform space per-ceived by the human eye, and it contains all the colorsseen by the human eye, we use the direction of chromaticaberration and chromatic aberration instead of the magni-tude and direction of the gradient. The paper proposes aSobel color difference operator, which can quickly calcu-late the chromatic aberration and chromatic aberrationdirection of local regions. In this paper, we use the Sobelcolor difference operator to calculate the Sobel color dif-ference amplitude and color difference direction.

Dx ¼ CD xi−1; j−1; xi−1; j−1� �þ 2CD xi; jþ1; xi; j−1

� �þ CD xiþ1; jþ1; xiþ1; j−1

� � ð4Þ

Dy ¼ CD xiþ1; j−1; xi−1; j−1� �

þ 2CD xiþ1; jþ1; xi−1; j� �

þ CD xiþ1; jþ1; xi−1; jþ1� �

a ð5Þ

CD xmn; xpq� � ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiLmn−L2pq

� �þ amn−a2pq� �

þ bmn−b2pq� �r

ð6Þ

The LAB values of the two pixels in the above equa-tion are (Lmn, amn, bmn) and (Lpq, apq, bpq), respectively.The magnitude and direction of the chromatic aberra-tion at (i, j) can be expressed as:

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CSD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiD2

x þ D2y

qð7Þ

φ ¼ arctan Dy=Dx� � ð8Þ

Then, we perform non-maximum suppression onthe Sobel color difference amplitude, traverse eachpixel on the Sobel color difference amplitude image,and interpolate to calculate the Sobel color differenceamplitude of two adjacent pixels in the current pixelgradient direction. If the current pixel’s Sobel colordifference magnitude is greater than or equal to thisvalue, the current pixel is a possible edge point;otherwise, the pixel point is a non-edge pixel point,and the image edge is refined to a pixel width. Theedge of the image is then extracted with a doublethreshold, and the non-maximum suppressed imageis thresholded with a high threshold and a lowthreshold, respectively. At the same time, by

combining the recursive methods, we use the weakedge E2 pixels to connect the discontinuities in thestrong edge E1. The results obtained on the basis ofedge processing are shown in Fig. 3.

3 ResultsIn order to study the effectiveness of the method,the performance of the algorithm is studied by con-structing a system platform. The system software isdeveloped on the Microsoft Visual C++ 6.0 platform,and SOLServer2000 is used as the application ofbackground database. The image is acquired by a linearray camera with a line frequency of 19K, and thesize is usually 2560 × 2048. Experimental machineconfiguration: the processor is Intel Core 2 Duo, thememory is l G, and the operating system is WindowsXP. The original image is shown in Fig. 4.In the experiment, the neural network image recogni-

tion algorithm was used as a comparison to carry out

Fig. 3 Effect image of edge recognition

Fig. 4 Original image of the test object

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analysis. In the actual analysis, we use Fig. 4 as the testobject for comparative analysis. First, the image is grays-caled, and the result is shown in Fig. 5. Among them,Fig. 5a is the processing result of the gradation of theneural network, and Fig. 5b is the processing result ofthe gradation of the algorithm of the present study.On the basis of grayscale processing, we take enhanced

processing on the image, and the result is shown in Fig. 6.Among them, Fig. 6a is an image enhancement effectimage of the neural network, and Fig. 6b is an enhance-ment effect image of the algorithm of the present study.After that, we perform edge processing on the image

to make the recognition object stand out further. Theresult is shown in Fig. 7. Among them, Fig. 7a is theedge recognition effect of the neural network image, andFig. 7b is the edge recognition effect of the algorithm ofthe present study.Based on the above analysis results, the English ad-

dress of the image is output through the system, and theobtained results are compared and analyzed. The outputof the recording system is shown in Table 1.

4 Discussion and analysisThe English address translation system proposed in thispaper collects the envelope image of the Chinese addressin English, locates the address area of the addressee, andidentifies the content of the address of the addressee.Simultaneously, this paper analyzes the content of theaddress and extracts the address information and usesthe string matching technology to translate the English

address into a Chinese system. In addition, this study ef-fectively integrates character recognition technology andmachine translation technology into the system. Finally,the research algorithm and neural network image pro-cessing method are compared and analyzed.As shown in Fig. 5a is a result of gray network pro-

cessing of the neural network, the grayscale processedimage is relatively blurred, and the grayscale color isrelatively hard, which is difficult to meet the subsequentrecognition requirements. Figure 5b is the result of thegrayscale processing of the algorithm in this study. Itcan be seen from the comparison of the sharpness thatthe grayscale processing of the research algorithm doesnot make the image too distorted, and the grayscale pro-cessing color is softer, which is beneficial to the subse-quent image processing.Shown in Fig. 6a is a neural network enhancement

effect, and the image has a certain improvement inthe definition based on the gray processing, but isstill relatively fuzzy, and the character recognition isdifficult. Figure 6b shows the enhanced picture of thealgorithm in this study. From the visual point ofview, the text is clearer and the background of thepicture has no effect on the recognition result.As shown in Fig. 7, for the edge recognition result,

from the result of the neural network processing ofFig. 7a, the text portion is basically recognized, butthe character definition has a problem, and the char-acter recognition is difficult. Figure 7b is the process-ing result of the algorithm of this study, the picture

Fig. 5 Comparison of processing results of image grayscale

Fig. 6 Comparison of the effects of image enhancement processing

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text is relatively clear and the background of the pic-ture has been completely eliminated and does notaffect the recognition of the picture text content.The results of neural network processing and the

processing results of the research algorithm areshown in Table 1. It can be seen from Table 1 thatthe algorithm of this study achieves 100% reductionin the English address recognition effect, and the ad-dress can be fully recognized only by the machine,so the algorithm of the present research can be usedfor automatic recognition of the machine system.However, the distortion of the neural network pro-cessing results is more serious, the machine cannoteffectively identify the English address, but also needsto be corrected by humans, so it is difficult to applyit to the automatic recognition of the machine.According to the above comparative analysis, the

proposed algorithm has certain advantages in imageprocessing and recognition results compared with thetraditional neural network recognition algorithm. Thealgorithm of this study can achieve 100% reductionof English address, meet the requirements of ma-chine automation identification, and has certain prac-tical effects, so it is worth promoting afterwards, andit is helpful for the development of automatic mailaddress recognition technology.

5 ConclusionThis study takes English mail as an example to carry outtranslation research of English image recognition and pro-poses corresponding research methods to promote the ap-plication of image recognition technology in mailsemantic recognition. For address image recognition, it isfirst necessary to extract the image frame by video orphotographing, thereby detecting it as an image. The

grayscale process discussed in this study refers to the con-version of images from RGB data to grayscale data, andthe weighted average grayscale algorithm is used for gray-scale processing. Meanwhile, there is a lot of text contenton the image on the image text, and the information inthe detail part is also very important. Therefore, this paperuses homomorphic filtering to enhance the image.Through homomorphic filtering, it is also possible tohighlight the details of the darker part of the image due touneven illumination, which is beneficial to the next imageprocessing. In this paper, the method of median filtering isadopted, which is easy to implement, the image processingeffect is quite good, and the algorithm is faster, which cansave recognition time to some extent. In addition, theSobel color difference operator is used to calculate theSobel color difference amplitude and color difference dir-ection, and then the image is edge-recognized, the text re-sult is recognized, and the English address result is finallyoutput. Finally, the performance of the proposed algo-rithm and the neural network algorithm are comparedwith the experimental analysis. The results show that theresearch has certain advantages in image processing andrecognition results and can achieve 100% reduction ofEnglish addresses, which meets the requirements of ma-chine automation identification.

AcknowledgementsThe authors thank the editor and anonymous reviewers for their helpfulcomments and valuable suggestions.

FundingThis research is supported by the project of hubei provincial teaching reformresearch (No. 2017501).

Availability of data and materialsPlease contact author for data requests.

Author’s contributionsThe author read and approved the final manuscript.

Competing interestsThe author declares that he has no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Fig. 7 Comparison of effects of image edge recognition

Table 1 Comparison of English address recognition results

Method Output result

Neural networks Cnlna Mtional NNacNnery Import55xport36,Jlansnan Road Dalian,116o23 People’sRepublic of China

Algorithm of this study China National Machinery Import&Export 36,Jianshan Road Dalian,116023 People’sRepublic of China

Wen EURASIP Journal on Image and Video Processing (2019) 2019:11 Page 8 of 9

Received: 26 October 2018 Accepted: 2 January 2019

References1. R. Canovas, J. Tomás, J. Lloret, et al., Statistical speech translation system

based on voice recognition optimization using multimodal sources ofknowledge and characteristics vectors. Comput. Stand. Interfaces 35(5),490–506 (2013)

2. I. Lénárt, Associations and verbal consciousness: an analysis based on fourEnglish and one Hungarian translation of Bulgakov’s novel: the Master andMargarita. Neohelicon 44(2), 487–504 (2017)

3. O. Koller, J. Forster, H. Ney, Continuous sign language recognition: towardslarge vocabulary statistical recognition systems handling multiple signers.Comput. Vis. Image Underst. 141, 108–125 (2015)

4. J.L. Fernández-Martínez, A. Cernea, Numerical analysis and comparison ofspectral decomposition methods in biometric applications. Int. J. PatternRecognit. Artif. Intell. 28(01), 1456001 (2014)

5. Du Y, Liu G, Feng G, et al. Speckle reduction in optical coherencetomography images based on wave atoms[J]. J. Biomed. Opt. 19(5):056009(2014)

6. T. Zhang, A. Chowdhury, N. Dhulekar, et al., From image to translation:processing the Endangered Nyushu Script. ACM Trans. Asian Low-ResourceLang. Inform. Proc. 15(4), 1–16 (2016)

7. G.M. Bidelman, S. Moreno, C. Alain, Tracing the emergence of categoricalspeech perception in the human auditory system. Neuroimage 79(6), 201–212 (2013)

8. C. Clemente, A. Balleri, K. Woodbridge, et al., Developments in target micro-Doppler signatures analysis : radar imaging, ultrasound and through-the-wall radar. Eurasip J. Adv. Sign. Proc. 2013(1), 47 (2013)

9. K. Alaerts, D.G. Woolley, J. Steyaert, et al., Underconnectivity of the superiortemporal sulcus predicts emotion recognition deficits in autism. Soc. Cogn.Affect. Neurosci 9(10), 1589 (2014)

10. I. Kapsouras, N. Nikolaidis, Action recognition on motion capture data usinga dynemes and forward differences representation. J. Vis. Commun. ImageRepresent. 25(6), 1432–1445 (2014)

11. A.D. Herekar, A.A. Herekar, A. Ahmad, et al., The burden of headachedisorders in Pakistan: methodology of a population-based nationwide study,and questionnaire validation. J. Headache Pain 14(1), 73–73 (2013)

12. C. Capek, D. Waters, B. Woll, et al., Hand and mouth: cortical correlates oflexical processing in British sign language and speechreading English. J.Cogn. Neurosci. 20(7), 1220–1234 (2014)

13. J. Chen, H. Cao, P. Natarajan, Integrating natural language processing withimage document analysis: what we learned from two real-worldapplications. Int. J. Doc. Anal. Recognit 18(3), 235–247 (2015)

14. C. Schaeffner, Evaluation in translation: critical points of translator decision-making. Transl. Stud. 6(3), 355–358 (2013)

15. L.N. Vieira, Indices of cognitive effort in machine translation post-editing.Mach. Transl. 28(3–4), 187–216 (2014)

16. J. Adhikari, M.C. Fitzgerald, SILAC-pulse proteolysis: a mass spectrometry-based method for discovery and cross-validation in proteome-wide studiesof ligand binding. J. Am. Soc. Mass Spectrom. 25(12), 2073–2083 (2014)

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