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RETRACTED ARTICLE: Research on English translation distortion detection based on image evolution

This article was retracted on 13 September 2022

This article has been updated


At present, there are serious distortions in the translation of image English characters. In order to alleviate this problem, this study improves the traditional algorithm, uses the Canny edge detection method as the edge detection method through experimental comparison and analysis, and combines the image evolution to analyze the English character translation of multiple complex images. Simultaneously, in this study, the closed space is used to fill the small holes in the target area, and some intrinsic characteristics of the text area are used to form the heuristic knowledge to limit the connected area, and the English candidate area is constructed for the image recognition algorithm of the image. Then, this study uses the English candidate area as the recognition area for translation recognition. The research shows that the algorithm has certain practical effects and can provide a theoretical reference for subsequent related research.

1 Introduction

In people’s life, there is no information about characters at all times, and this information is generally divided into two types of information: print and handwriting. The characters of the printed body have a certain regularity, and each character has a certain template, so in the process of computer recognition, the character recognition rate is high. Because of the difference between people and people, handwritten characters do not have a unified template for each character, which causes the recognition rate of the computer to be low in the process of recognizing handwritten characters and the recognition speed is slow. For English fonts, there are only 26 English letters, some of which are similar, so it is easy to be confused during the recognition process. Especially in the machine identification, there are obvious automatic recognition errors. Based on this, it is necessary to improve the distortion of English translation [1].

Since the great breakthrough in character recognition by scientist Tausheck in 1929, character recognition has become a hot issue in pattern recognition after years of unremitting efforts. Since the 1950s, the study of optical characters has slowly begun to develop. In the early 1960s, there were several commonly used identification systems on the market, including optical character recognition programs developed by NCR, Farrington, and IBM. Although these developed systems are able to initially classify characters, these programs have many drawbacks in many functions [2]. In order to solve these problems, after about 10 years of research, Parks et al. proposed to use the topology method to extract the character structure of the character first and then identify it [3]. After several years of development, by 1980, Japan had achieved certain results in character recognition, and based on the research results, developed the first postal code sorter. In the early 1990s, Vapnik et al. proposed a support vector machine based on mathematical statistics. This proposal opened a new way for pattern recognition and made a great contribution to pattern recognition. In the following years, the neural network was also associated with image recognition, and good results were also achieved [4].

China’s research work on OCR technology started late. In the 1970s, Dai Wei, an academician of the Institute of Automation of the Chinese Academy of Sciences, led the handwritten character recognition. In 1974, the handwritten digit recognition system studied was applied to the automatic sorting of postal letters [5]. In the late 1970s, the research on Chinese character recognition began. By 1986, the research on Chinese character recognition entered a substantive stage and achieved great results, and many research units have successively launched Chinese OCR products. The National “863 Program” has given great support to the research of OCR technology and promoted the achievement of OCR’s major achievements [6]. At present, online handwritten Chinese character recognition technology is quite mature, and a number of representative products have appeared on the market. At the same time, offline print identification also has mature products, and the recognition rate can basically meet the practical requirements [7]. In addition, the research on offline handwritten Chinese character recognition has made great progress, and the small character set word recognition technology is relatively more mature. For example, the financial Chinese character recognition system developed by Beijing Post and Telecommunications Research Institute in 1998 obtained a 99.7% recognition accuracy rate in the National 863 tests. At the same time, Chinese character recognition methods for large character sets, such as cosine shaping transformation methods, have also achieved high precision [8]. The theory and technology of word recognition are generated by strong social demand and continue to develop. However, it can be said that no recognizer has achieved perfect recognition. In the past few decades, researchers have proposed a number of identification methods. Letter recognition belongs to the category of pattern recognition, which is a specific problem of pattern recognition, and letter recognition has special requirements different from other pattern recognition. In addition to requiring high recognition accuracy and reliable work, letter recognition requires high recognition efficiency and fast recognition speed, which requires accurate mathematical modeling [9].

With the continuous advancement of technology, several new recognition technologies will be rapidly developed in the foreseeable future, and the optical character recognition system will be continuously improved [10, 11]. (1) Recognition method based on fuzzy technology—because the characters themselves, especially the handwritten characters, vary greatly in the font type, which leads to great uncertainty in the text recognition, the concept of fuzzy mathematics is naturally cited in the field of pattern recognition. In 1976, Rosenfeld et al. proposed a scene identification relaxation algorithm. In 1977, Jain et al. used the fuzzy set theory to analyze complex images, realized the detection of moving targets, and began the application of fuzzy mathematics in image recognition [12]. (2) Post-processing techniques combined with semantic understanding—in contrast to the pre-recognition pre-processing, this technique can post-process the results of the recognition and improve the correct rate of recognition. In the process of analyzing human beings in recognizing words, words are generally understood in conjunction with context. Therefore, when the computer recognizes the text, the recognition result can be corrected by combining the context information of the single word on the basis of identifying the word, and the word or even the sentence is used as the result of the recognition. According to the statistical information of the language and text, it is possible to determine the candidate character set that may follow a certain text, thereby narrowing the search scope and simplifying the calculation. The problem of this technology combined with context information identification mainly focuses on how to efficiently organize candidate character subsets and realize the rapid positioning of candidate characters [13]. (3) Comprehensive integration of multiple strategies—in the field of OCR, although new algorithm ideas continue to emerge, the use of only one identification method in an efficient OCR system cannot meet the requirements of reality [14]. The ability to identify a single strategy is limited, so multiple strategies are used to achieve complementary advantages, and the use of character information in multiple angles is the direction of OCR development. The integration strategies often used in this direction are a variety of integrated methods such as voting, probabilistic, Dempster-Sharer, and behavioral knowledge space. Taking the voting method as an example, as the name suggests, each identification strategy has a ballot. For each strategy with the same character, each of its own results produces a vote. After all the strategies are voted, the most recognized result is the final recognition result. Obviously, human resources are needed in this integrated approach. On the one hand, the completion of various algorithms requires human resources. On the other hand, if the parallelism between the various algorithms is not good, the total execution time will be multiplied [15].

From the above analysis, we can see that there are some problems in the process of image translation in English; especially in the actual translation process, there will be distortion. For these distortion problems, improved identification methods are needed to reduce the distortion rate. Based on this, this study combines image recognition technology to improve the traditional algorithm and strive to improve the image translation effect.

2 Research methods

2.1 Image edge detection

Edge detection is the basis of all algorithms that segment images based on edges. The edge of the image is the boundary between different regions and regions in the image, and it is also the part where the local features of the image change significantly. It is represented by a discontinuous pattern of local characteristics of the target, such as sudden changes in luminance values, sudden changes in color, and mutations in texture features. There are two characteristics at the edge of the image: direction and amplitude characteristics. In general, along the edge, the gray level of the pixel changes relatively gently, while the gray level changes perpendicular to the edge. The edge is the most dramatic change in the gray value on the image, which is reflected in the mathematical expression, the place where the function gradient is relatively large. Therefore, the idea of edge detection is mainly focused on the study of better derivative operators. The method of edge detection mainly focuses on calculating the first derivative or the second derivative of the gray value of the image; the edge point of the image corresponds to the peak point of the first-order differential image and corresponds to the zero-crossing point on the second-order differential image. The general image edge detection method has three steps: image filtering—filters are used to improve the performance of noise-related edge detectors; image enhancement—this step is usually done by calculating the magnitude of the gradient; and image detection—this step is mainly to determine which points are edge points. The simplest edge detection judgment is based on the gradient magnitude. Gradient-based image edge detection operators have two main categories: the edge detection operator of the first-order derivation and the edge detection operator of the second-order derivation.

The Roberts operator provides a simple approximation for gradient magnitude calculations [16]:

$$ G\left(i,j\right)=\left|f\left(i,j\right)-f\left(i+1,j+1\right)\right|+\left|f\left(i,j\right)-f\left(i+1,j\right)\right|-\left(i,j+1\right) $$

Sobel operator uses 3 × 3 neighborhood to avoid calculating gradients at interpolation points between pixels. It can be expressed as:

$$ M=\sqrt{s_x^2+{s}_y^2} $$

Among them, sx = (a2 + ca3 + a4) − (a0 + ca7 + a6), sy = (a2 + ca3 + a4) − (a0 + ca7 + a6) and constant coefficient c = 2. The Prewitt operator is similar to the Sobel operator, except that its constant coefficient is c = 1. The edge operator of the first derivative sometimes causes too many edge points to be detected, and the edge of the detection is thick. However, the zero-crossing point of the second derivative corresponds to the local maximum of the first derivative. Therefore, we use the operator of the second derivative to find the points corresponding to the local gradient maximum and determine that they belong to the edge points, which can detect more accurate edges. The Laplacian operator is a commonly used second-order derivative operator. The formula is:

$$ {\nabla}^2f=\frac{\partial^2f}{\partial {x}^2}+\frac{\partial^2f}{\partial {y}^2} $$

Among them,\( \frac{\partial^2f}{\partial {x}^2}=f\left(i,j+1\right)-2f\left(i,j\right)+f\left(i,j-1\right) \), \( \frac{\partial^2f}{\partial {y}^2}=f\left(i+1,j\right)-2f\left(i,j\right)+f\left(i-1,1\right). \) When the Laplacian output shows a zero-crossing, it indicates that there is an edge. The LoG operator is obtained by convolution operation, that is:

$$ h\left(x,y\right)=\left[{\left(\frac{x^2+{y}^2-2{\sigma}^2}{\sigma^4}\right)}^{\frac{x^2+{y}^2}{2{\sigma}^2}}\right]\times f\left(x,y\right) $$

The Canny operator proposes three criteria for evaluating the performance of detection performance: SNR principle (the real edge is lost as little as possible and as far as possible avoiding detecting non-edge points as edges), positioning accuracy criterion (the detected edge should be as close as possible to the real edge), and single-edge response criterion (there is a unique response to each edge point, which is the edge of a single pixel width). According to the three criteria, the best edge can be obtained. As shown in Fig. 1, the original image and the edge detection results of Sobel, Prewitt, Roberts, LoG, and Canny are numbered sequentially from a to f. It can be found that the edge extraction ability of Canny operator is the most satisfactory, and it is better adapted to the change of detection information, ensuring the continuity and closure of the edge.

Fig. 1
figure 1

Edge detection results

What is given after the edge detection is the binary edge image es. Binarization of edge images is an important issue. If the threshold is too large, some text edges may be missed, and if the threshold is too small, more non-text edges may be treated as text edges, causing more false detections. In order to achieve good results in binarization, the edge image is first morphologically filled, the holes are removed to remove noise, and then adaptive threshold segmentation is performed to obtain a binary image. Mathematical morphology is an operation based on mathematical sets. Its basic principle is to use structural elements with certain characteristics to measure and extract similar shapes in images, so as to achieve the purpose of image analysis and recognition. Morphological operations on the image simplify the data of the image and eliminate irrelevant structures while maintaining the basic shape characteristics of the image. In the usual image processing, there are four basic morphological operators: corrosion, expansion, open operation, and closed operation.

The corrosion operation is defined as [17]:

$$ A\ominus B=\left\{x|{(B)}_x\subseteq A\right\} $$

The main function of the corrosion operation is to mark the interior of the image where the defined structural elements can be filled. Corrosion operations can remove targets smaller than structural elements (such as burrs, small bulges), so that different sizes of objects can be removed from the original image by selecting structural elements of different sizes. At the same time, corrosion can also eliminate object boundary points. If there is a small connection between the two targets, then if the structural elements are chosen to be large enough, the two targets can be separated by a corrosion operation.

The expansion operation is defined as follows:

$$ A\bigoplus B=\left\{x|{\left(\widehat{B}\right)}_x\bigcap A\ne \varnothing \right\} $$

It can thicken or lengthen the object of the operation. At the same time, the expansion operation can effectively expand the target area. Using the same structural element to take the first corrosion operation on the image and then taking the expansion is called open operation:

$$ {A}^{{}^{\circ}}B=\left(A\ominus B\right)\bigoplus B $$

The open operation can remove the small branches that exist in the target area, disconnect the narrow connections between the targets, and make the outline of the object smoother. The closed operation is an operation that takes expansion first and then takes corrosion:

$$ A\bullet B=\left(A\bigoplus B\right)\ominus B $$

By using the closing operation, small holes in the target area can be filled, narrow gaps are formed, and elongated bends are formed. After marginalizing the image, there is a gap between the resulting characters, so the filling is used to fill the gaps using mathematical morphology. First, the closed operation is used to remove the holes, fill the cat holes and gaps, and then fill the holes. After that, the open operation is used to remove the noise. In general, the false edges that remain in the edge image that are not part of the text area are small, isolated points. The undetected text edge exists in the periphery of the extracted text edge, so the structural element is taken as 3 × 3. The processing results are shown in Fig. 2.

Fig. 2
figure 2

Morphological filling results

2.2 Image text extraction technology

The 0-pixel set or the 1-pixel set that communicates with each other in a binary image is referred to as a connected component. There may be multiple connected components in one image after segmentation, and each connected component corresponds to a target image region, and the process of assigning corresponding labels to each target image region is called a mark. Common connectivity area marking algorithms mainly have four connections and eight connections. The eight-connected region means that from each pixel in the region, it can reach any pixel in the region through eight directions under the premise of not getting out of the region, namely, eight directions of up, down, left, right, upper left, upper right, lower left, and lower right. However, the four connected areas are only connected in the four directions of up, down, left, and right. In this paper, the candidate text area is marked by the eight-neighbor labeling algorithm. The background is marked as 0, the first connected area is marked as 1, and the second connected area is marked as 2, and so on. After marking, the attribute characteristics of each connected domain can be calculated, such as perimeter and area.

After morphological filling, the edges are closed, but the character regions are overfilled, so the threshold segmentation method is used to obtain a sharper segmentation image. Threshold segmentation is an important technique for data pre-processing. According to the number of selected thresholds, it can be classified into global threshold segmentation and local thresholding. For globalized threshold segmentation, the selected threshold is applied to each pixel of the entire image, and the processing speed is relatively fast, which is effective for better quality images. Especially for images with bimodal histograms (one peak corresponds to the background in the image and the other peak corresponds to the target of the image), this method works better. However, if the background is complicated, it is often impossible to take into account the situation throughout the image, and the segmentation effect will be affected. Another method is local thresholding, which sets multiple binarization thresholds. It is typically determined dynamically by the pixel gray value and the local grayscale characteristics of this pixel region:

$$ {f}_k\left(i,j\right)=\left\{\begin{array}{c}1\ {f}_k\ge {T}_k\left(i,j\right)\\ {}0\kern0.5em {f}_k<{T}_k\left(i,j\right)\end{array}\right. $$

The subscript k indicates the kth area. Since each area contains content that is quite different from other areas, the relationship between the area and the area and the relationship between each small area and the entire image must be considered. If the judgment is only based on the human eye, it is not only time-consuming and labor-intensive but also affects the segmentation effect of the image due to the subjective cognitive error of the person. For the connected regions of the markers, the corresponding regions are found in the grayscale image g for processing. For each connected area k, the threshold is calculated as follows:

$$ \gamma =\frac{\sum (g.s)}{\sum s} $$

Among them, g is the grayscale image and s is given by:

$$ s=\max \left(\left|g1\ast g\right|,\left|g2\ast g\right|\right) $$

Among them, g1 = [− 1,0,1], g2 = [− 1,0,1], means T, and · represents two-dimensional linear convolution. The difference between the x-direction and the y-direction is obtained separately, and then the absolute value is stored in s. According to the threshold γ, the binarized image e is obtained as shown in Fig. 3:

$$ e=\left\{\begin{array}{c}1,\mathrm{if}\ g>\gamma\ \mathrm{and}\ k>0\\ {}0,\mathrm{otherwise}\end{array}\right. $$
Fig. 3
figure 3

Threshold segmentation results

It can be seen in Fig. 3 that after the threshold segmentation, the features are more prominent, and the target recognition is more convenient. At the same time, with some methods based on global threshold or optimal threshold segmentation, this adaptive threshold segmentation is not sensitive to the effects of illumination conditions and reflections. After the threshold segmentation forms the binarized image e, the connected regions formed by the self-color pixel points in the image are re-marked to obtain a candidate text region.

Due to the complexity and variety of color images, some noise points or noise curves are inevitably present in the candidate text regions. Therefore, it is necessary to form some heuristic knowledge in combination with some inherent characteristics of the text area to limit the connected area. If it does not satisfy the following conditions, the connected area is regarded as noise and is eliminated.

3 Results

This paper draws on the traditional image recognition detection ideas and makes appropriate changes to facilitate the detection of English. At the same time, through the recognition algorithm for image English proposed in this paper, the English candidate region is constructed, and then the English candidate region is used as the recognition region for translation recognition. The effect of detection and recognition in English in natural scenes is shown in Fig. 4.

Fig. 4
figure 4

English image detection and recognition effect

As shown in Fig. 4, Fig. 4a is a video image in a complex environment. It can be seen from the image that the picture noise is serious, the text in the picture is affected by the complex background, and the recognition is difficult. The result of text recognition segmentation by the genetic neural network is shown in Fig. 4b. The English recognition result of the research algorithm of this paper is shown in Fig. 4c. Figure 4 is a text detection under complex environmental conditions. The following is a description of the complex background of the image of the object itself. The results obtained are shown in Fig. 5.

Fig. 5
figure 5

English recognition of pictures with complex background

As shown in Fig. 5, Fig. 5(a) is an original video image, and the background of the image is relatively complicated and belongs to the background of the building, so the text segmentation is very difficult. Figure 5(b) shows the result of text recognition segmentation by the genetic neural network, and Fig. 5c shows the character recognition result of the algorithm of the present study.

After that, the English text recognition under different text interactions is performed. This study selects the case where English letters are mixed with other characters, and the obtained recognition results are shown in Fig. 6.

Fig. 6
figure 6

English recognition of mixed images in English and Chinese

As shown in Fig. 6, Fig. 6a is an original video image, and the background of the image is relatively complicated, which is a technical parameter image of a vortex mixer, so that text segmentation is very difficult. Figure 6b shows the result of text recognition segmentation by the genetic neural network, and Fig. 6c shows the character recognition result of the algorithm of the present study.

Finally, in order to compare the comprehensive performance of the algorithm, the speed, accuracy, image sharpness, image de-noise rate, and image distortion rate are used as contrast parameters to compare the research algorithm of this paper with the genetic neural network algorithm. The results obtained are shown in Table 1.

Table 1 Comparison of English image translation performance between the algorithm of this paper and the genetic neural network algorithm

4 Discussion and analysis

At present, the translation software is for displaying text. If the text appears in the image, this translation software cannot do anything about it. Although some visual-based semi-automatic or automated translation systems have emerged, most are based on server and client architectures. These systems require users to upload images and perform text detection on the server side to extract translations, so translation results cannot be provided in real time. At the same time, the translation results are simply superimposed on the screen and do not achieve good visual effects. Based on this, this study proposes an image English translation algorithm based on image evolution, which can detect English in a variety of images.

Through the experimental analysis, the performance of the algorithm is analyzed. Simultaneously, the comparison of image recognition results and performance parameters can be used to draw corresponding conclusions. As can be seen in Fig. 4, the genetic neural network basically detects the text with clear outline and less interference in the figure and is selected as a single English area by the construction of the English area. However, for those words whose glyphs are not clear enough or are relatively small in size, the method has not been successfully detected. At the same time, not only the text portion of the image is recognized, but also other image portions are also emitted, so it has a certain influence on the English recognition. In the face of a complex background, large illumination impact or serious graphic deformation, the detection method proposed in this paper cannot accurately detect the text area and eliminate the influence of other factors in the background. Therefore, the proposed algorithm has a better translation and recognition effect on English text in a fuzzy environment.

Figure 5 shows the text recognition in a complex background. Through comparative analysis, it can be seen that the neural network algorithm accurately detects the English located above and constructs it into a single complete Chinese character. However, because the angle of the text in the picture is tilted and the background contains a lot of horizontal and vertical building disturbances, the Chinese characters and the background of the building also are detected, and they are combined with the noise data to form a larger non-English area. The text located below the image is disturbed by the entrance of the building, and its font is not clear in the figure, and its background is also a strip-shaped building. Therefore, when the stroke area is screened, it is filtered out as a large area together with the background. In the actual identification of the research algorithm of this paper, in addition to the effective elimination of the building part, the Chinese text part can also be eliminated. At the same time, the algorithm only retains the English part, and the English text part is clearly expressed, and the recognition effect is good.

Figure 6 shows the mixed image recognition in Chinese and English. The image background is complex and is a technical parameter image of a vortex mixer. Therefore, the text segmentation is very difficult. Through research, it is found that the genetic neural network algorithm recognizes both Chinese and English from the image in image segmentation, thus causing Chinese and English to be mixed in the recognition result. However, the algorithm of this study can identify all the English parts of the image, and the Chinese part is filtered out along with the background.

From the performance experiment results, the accuracy of the method for detecting and identifying English in natural scenes is 99.3%, which is less than 100%. Therefore, further research and improvement are needed. From the current status quo, compared with the current advanced genetic neural network algorithm recognition results, the algorithm leads the genetic neural network algorithm in recognition speed, accuracy, image sharpness, image de-drying rate, and image distortion rate. First of all, in the recognition speed, the algorithm is far ahead of the genetic neural network algorithm, and secondly, it is close to 100% accuracy, which can be initially applied to practice. In addition, the algorithm can ensure the image distortion rate is low after recognition, ensure the image has certain clarity, and effectively eliminate the noise in image recognition. Therefore, it is a good image recognition algorithm for images.

5 Conclusion

Aiming at a variety of complex images under the condition of image evolution, this study combines image recognition technology to improve the traditional algorithm and strive to improve the translation of image English. After comparing the edge detection algorithm, it is found that the edge extraction ability of the Canny operator is the most satisfactory, and it is better adapted to the change of detection information, which ensures the continuity and closure of the edge. At the same time, in order to achieve good results in binarization, the edge image is first morphologically filled, the holes are removed to remove noise, and adaptive threshold segmentation is performed to obtain a binary image. By using the closing operation, small holes in the target area can be filled, and narrow gaps are connected to form elongated bends. At the same time, after the image is marginalized, there is a gap between the characters obtained, so the filling is used to fill the gaps by the idea of mathematical morphology. Due to the complexity and variety of color images, some noise points or noise curves are inevitably present in the candidate text regions. Therefore, this study combines some inherent characteristics of text regions to form heuristic knowledge to limit connected regions. If it does not satisfy the following conditions, the connected area is regarded as noise and is eliminated. In addition, this paper draws on the traditional image recognition detection ideas and makes appropriate changes to use English detection. Through the recognition algorithm for image English proposed in this paper, the English candidate region is constructed, and then the English candidate region is used as the recognition region for translation recognition. From the performance experiment results, the performance of the proposed algorithm is good and meets the research expectations.

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Cui, L. RETRACTED ARTICLE: Research on English translation distortion detection based on image evolution. J Image Video Proc. 2019, 25 (2019).

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