在乳胶中创建完全合理的表格（背页）[关闭]

Question

我在 Latex 中创建了一个表格，但表格中的文本或内容没有完全合理。所以我该怎么做？为了更好的可视化，我在下面分享了我的乳胶代码-

\begin{longtable}{|c|l|l|}
\caption{Summary Table}\\ 
\hline
\textbf{Serial No.}  & \multicolumn{1}{c|}{\textbf{Name} }                                                                                                                                                    & \multicolumn{1}{c|}{\textbf{Description and Scope} }                                                                                                                                                                                                                                                                                                                                                                                                                                                          \endfirsthead 
\hline
1.                   & \begin{tabular}[c]{@{}l@{}}Skin Disease Recognition \\ Method Based on Image \\ Color and Texture \\ Features \end{tabular}                                                            & \begin{tabular}[c]{@{}l@{}}The work was on detecting the image of \\ skin where the Partial Differentiation \\ Equation (PDE) had been used. By the \\ color feature and text feature the \\ recognition rate can reach to 90\% and \\ more. For skin disesase detection color \\ feature is very much important. \end{tabular}                                                                                                                                                                               \\ 
\hline
2.                   & \begin{tabular}[c]{@{}l@{}}Skin Lesion Analysis\\ Toward Melanoma \\ Detection: A Challenge \\ at the 2017 International \\ Symposium on \\ Biomedical Imaging \\ (Isbi) \end{tabular} & \begin{tabular}[c]{@{}l@{}}The work was on developing algorithms \\ for automated diagnosis of melanoma, \\ the most lethal skin cancer, Thresholded \\ Jaccard, Balanced Thresholded Jaccard,\\ multipartition test sets etc had been used. \\ The better capture of the proportion of \\ segmentation failures. Alongside accuracy \\ for signicant changes in participant ranking \\ versus other metrics. An effective way to \\ differentiate the ability of algorithms to \\ generalize. \end{tabular}  \\ 
\hline
3.                   & \begin{tabular}[c]{@{}l@{}}Automating Skin Disease \\ Diagnosis Using Image\\ Classification \end{tabular}                                                                             & \begin{tabular}[c]{@{}l@{}}PSL images analysis based on texture and \\ morphological features of the images.\\ Maximization of the large availability of \\ ubiquitous devices and elicitation of past \\ skin cancer diagnosis \end{tabular}                                                                                                                                                                                                                                                                 \\ 
\hline
4.                   & \begin{tabular}[c]{@{}l@{}}A Real Time Image\\ analysis System to \\ Detect Skin Diseases \end{tabular}                                                                                & \begin{tabular}[c]{@{}l@{}}A method for automatic prevention and \\ detection of Psoriasis, Melanoma,\\ Dermatophytes. The accuracy of it is 90\% \\ By this paper we get the idea of automated \\ skin disease system. \end{tabular}                                                                                                                                                                                                                                                                         \\ 
\hline
5.                   & \begin{tabular}[c]{@{}l@{}}Digital Dermatology\\ Skin Disease Detection \\ Model using Image \\ Processing \end{tabular}                                                               & \begin{tabular}[c]{@{}l@{}}A novel method where a patient can \\ self-diagnose using a mobile phone in rural \\ areas.By inputing an image the disease can \\ be recognized. By providing a feasible \\ solution for skin disease detection it gives \\ up to 80\% efficiency. Digital System gives \\ better result in diagnosing than analog \\ system. \end{tabular}                                                                                                                                       \\ 
\hline
6.                   & \begin{tabular}[c]{@{}l@{}}Automated System for \\ Prediction of Skin\\ Disease using Image \\ Processing and Machine \\ Learning \end{tabular}                                        & \begin{tabular}[c]{@{}l@{}}Determining skin disease (Psoriasis and \\ Acne) based on symptoms using KNN \\ algorithms and also using color and \\ texture features. Out of 5 skin diseases \\ used, the detection rate of Eczema and \\ Psoriasis 92.5\% and 91.6\% respectively. \\ Texture Feature analysis with Machine \\ Learning can give much better result than \\ any methods. \end{tabular}                                                                                                         \\ 
\hline
7.                   & \begin{tabular}[c]{@{}l@{}}Skin Disease Recognition \\ Using Texture Analysis \end{tabular}                                                                                            & \begin{tabular}[c]{@{}l@{}}An automated detection of three classes\\ of skin diseases (Eczema, Impetigo, and \\ Psoriasis) by analyzing textures and \\ obtained from a collection of medical \\ images based on GLCM. Overall performance is \\ calculated in terms of 80\% accuracy, 71.4\% \\ of sensitivity and 87.5\% of specificity. \\ GLCM had been used for getting more \\ appropriate result in detecting skin disease. \end{tabular}                                                              \\ 
\hline
8.                   & \begin{tabular}[c]{@{}l@{}}Dermatological Disease \\ Detection Using Image\\ Processing and Machine \\ Learning \end{tabular}                                                          & \begin{tabular}[c]{@{}l@{}}The work was divided into two phases \\ (computer vision and machine learning). \\ Identification of skin diseases with \\ accuracies of up to 95\% When Image \\ Processing and Machine Learning were \\ combined then the accuracy rate grew \\ so high. \end{tabular}                                                                                                                                                                                                           \\ 
\hline
9.                   & \begin{tabular}[c]{@{}l@{}}Dermatological disease \\ diagnosis using color-skin \\ images \end{tabular}                                                                                & \begin{tabular}[c]{@{}l@{}}An automated dermatological diagnostic \\ system. Diseased skin detection accuracy \\ of 95.99\% and disease identification \\ accuracy of 94.016\% Color skin images \\ give advantage for detecting skin disease \\ accurately. \end{tabular}                                                                                                                                                                                                                                    \\ 
\hline
10.                  & \begin{tabular}[c]{@{}l@{}}Comparison of machine \\ learning algorithm for \\ skin disease classification \\ using color and texture \\ features \end{tabular}                         & \begin{tabular}[c]{@{}l@{}}Comparison of K-means clustering and \\ markercontrolled watershed algorithm \\ with Fuzzy C-means clustering and \\ marker controlled watershed algorithm. \\ Integration of K-means clustering with \\ marker controlled watershed algorithm \\ gave better segmentation. \end{tabular}                                                                                                                                                                                          \\ 
\hline
11.                  & \begin{tabular}[c]{@{}l@{}} An Intelligent System \\ to Diagnosis the Skin\\ Disease \end{tabular}                                                                                      & \begin{tabular}[c]{@{}l@{}} An analysis of people’s behavior and \\ emotion were done for detecting skin \\ diseases.Obtaining some parameter value \\ through active contour method of infected \\ skin disease like acne and psoriasis. \\ Parameter values are good for analyzing \\ skin disease symptoms properly. \end{tabular}                                                                                                                                                                          \\
\hline
\end{longtable} 

Answer 1

不要在您的longtable 中使用tabulars。相反，选择一个固定宽度的paragraph 列，换行应该自然发生，使这些列中的文本完全对齐：

\documentclass{article}

\usepackage{longtable}

\begin{document}

\begin{longtable}{|c|p{.3\linewidth}|p{.5\linewidth}|}
  \caption{Summary Table} \\ 
  \hline
  \textbf{Serial No.}  & \multicolumn{1}{c|}{\textbf{Name}} & \multicolumn{1}{c|}{\textbf{Description and Scope}}
  \endhead
  \hline
  1. & Skin Disease Recognition Method Based on Image Color and Texture Features & 
    The work was on detecting the image of skin where the Partial Differentiation 
    Equation (PDE) had been used. By the color feature and text feature the 
    recognition rate can reach to 90\% and more. For skin disesase detection color 
    feature is very much important. \\
  \hline
  2. & Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (Isbi) & 
    The work was on developing algorithms for automated diagnosis of melanoma, the 
    most lethal skin cancer, Thresholded Jaccard, Balanced Thresholded Jaccard, 
    multipartition test sets etc had been used. The better capture of the proportion 
    of segmentation failures. Alongside accuracy for signicant changes in participant 
    ranking versus other metrics. An effective way to differentiate the ability of 
    algorithms to generalize. \\
  \hline
  3. & Automating Skin Disease Diagnosis Using Image Classification & 
    PSL images analysis based on texture and morphological features of the images. 
    Maximization of the large availability of ubiquitous devices and elicitation of 
    past skin cancer diagnosis \\
  \hline
  4. & A Real Time Image analysis System to Detect Skin Diseases & 
    A method for automatic prevention and detection of Psoriasis, Melanoma, 
    Dermatophytes. The accuracy of it is 90\% By this paper we get the idea of 
    automated skin disease system. \\
  \hline
  5. & Digital Dermatology Skin Disease Detection Model using Image Processing & 
    A novel method where a patient can self-diagnose using a mobile phone in rural 
    areas. By inputing an image the disease can be recognized. By providing a feasible 
    solution for skin disease detection it gives up to 80\% efficiency. Digital 
    System gives better result in diagnosing than analog system. \\
  \hline
  6. & Automated System for Prediction of Skin Disease using Image Processing and Machine Learning &
    Determining skin disease (Psoriasis and Acne) based on symptoms using 
    KNN algorithms and also using color and texture features. Out of 5 skin diseases 
    used, the detection rate of Eczema and Psoriasis 92.5\% and 91.6\% respectively. 
    Texture Feature analysis with Machine Learning can give much better result than 
    any methods. \\
  \hline
  7. & Skin Disease Recognition Using Texture Analysis & 
    An automated detection of three classes of skin diseases (Eczema, Impetigo, 
    and Psoriasis) by analyzing textures and obtained from a collection of medical 
    images based on GLCM. Overall performance is calculated in terms of 80\% accuracy, 
    71.4\% of sensitivity and 87.5\% of specificity. GLCM had been used for getting 
    more appropriate result in detecting skin disease. \\
  \hline
  8. & Dermatological Disease Detection Using Image Processing and Machine Learning & 
    The work was divided into two phases (computer vision and machine learning). 
    Identification of skin diseases with accuracies of up to 95\% When Image 
    Processing and Machine Learning were combined then the accuracy rate grew 
    so high. \\
  \hline
  9. & Dermatological disease diagnosis using color-skin images & 
    An automated dermatological diagnostic system. Diseased skin detection accuracy 
    of 95.99\% and disease identification accuracy of 94.016\% Color skin 
    images give advantage for detecting skin disease accurately. \\
  \hline
  10. & Comparison of machine learning algorithm for skin disease classification using color and texture features & 
    Comparison of K-means clustering and marker controlled watershed algorithm 
    with Fuzzy C-means clustering and marker controlled watershed algorithm. 
    Integration of K-means clustering with marker controlled watershed algorithm 
    gave better segmentation. \\
  \hline
  11. & An Intelligent System to Diagnosis the Skin & 
    An analysis of people's behavior and emotion were done for detecting skin 
    diseases. Obtaining some parameter value through active contour method of 
    infected skin disease like acne and psoriasis. Parameter values are good 
    for analyzing skin disease symptoms properly. \\
  \hline
\end{longtable} 

\end{document}