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\caption{Monolingual Online/Offline Recognition Systems}
\label{Tab:2}
%\hline\noalign{\smallskip}
\multicolumn{1}{l}{Author} &
\multicolumn{1}{c}{Script} &
\multicolumn{1}{c}{Text mode} &
\multicolumn{1}{c}{classifier} &
\multicolumn{1}{c}{Dataset} &
\multicolumn{1}{c}{Results}
\\[0.5ex]\hline \hline \\[-2ex]
\endfirsthead
\multicolumn{6}{c}{{\tablename} \thetable{} -- Continued}\\[0.5ex]
\hline \hline \\[-2ex]
\multicolumn{1}{c}{Author} &
\multicolumn{1}{c}{Script} &
\multicolumn{1}{c}{Text mode} &
\multicolumn{1}{c}{classifier} &
\multicolumn{1}{c}{Dataset} &
\multicolumn{1}{c}{Results}
\\[0.5ex]\hline \hline \\[-2ex]
\endhead
Rabi et al (2017) & Cursive Arabic & Offline Handwritten & HMM & IFN/ENIT &87.93\% \\ \\
Roy et al (2017) & Arabic & Offline,Handwritten & Deep belief network with HMM & IFN/ENIT & 89.46\% \\
Iwana et al(2017) & Latin & Online & DTW & UNIPEN digits data & 96.67\% \\
Sen et al (2017) & Bangla & Online & & Handwritten 10,000 character dataset & 95.57\% \\
Bhattacharya et al(2016) & Bangla& Online & & 33453 word samples written by 31 writers & 94.3\% \\
Dash et al (2016) & Bangla & Offline & Binary external symmetry axis constellation with Boolean matching & ISI kolkata and IITBBS database for Odia numerals, ISI Kolkata bangle numeral and IITBBS odia character database & 99.35\%,98.9\%,99.48\% and 95.01\% respectively accuracy 0.02528 s, 0.02615 s, 0.02392 s, 0.03791s resptive time \\
Pengchao et al (2016) & Chinese & Printed,offline & CNN & 2789965 samples of GB2312-80 database & 99.90\% \\
Zhang et al(2016) & Chinese & Online & Deep CNN & CASIA-OLHWDB 1.0, CASIA-OLHwDB 1.1 & 98.44\% and 98.05\% \\
Sundram et al 2015 & Tamil & Online & Expert SVMs & 15000 handwritten isolated words & 93.0\% and 81.6\% at symbol and word level respectively. \\
Suyyagh et al (2015) & Arabic & Offline Handwritten & FPGA & IFN/ENIT & 20 times faster than the software implementation and is less accurate by only 2.8\% \\
Arora et al (2014) & Gurumukhi & Offline Printed & SVM with linear and polynomial kernel & 7257 different images & 92.38\% for linear, 85.38\% for polynomial \\
Nguyen et al(2014) & English & Online & SVM with semi incremental approach & IAM-OnDB & 65.70\% WRR\\
Bral et al(2014) & Bangla &Online Handwritten & 5890 training set, 3534 test set & 91.6\% \\
A et al (2014) & Devanagari & Online & HMM with Nearest Neighbor classifier & 32,192 samples of 47 characters & 91.38\% \\
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答案1
您缺少一个\\来结束标题后的行：
\caption{Monolingual Online/Offline Recognition Systems}
\label{Tab:2} \\
答案2
除了缺失之外，\\以下 MWE 还对您的表格提供了一些改进：
ltablex支持在多页表中使用 的tabularx列X- 使用
p列代替来l节省水平空间 - 删除多余的
\multcolumn{1}命令 - 引入
siunitx数字和单位之间的一致间距 - 用s替换
\hrule并包围间距命令\midrule - 在条目之间添加空白作为引导眼睛
- 减小字体大小以节省空间
- 添加了一些缺失的空格
- ...
可能还需要进一步改进。
\documentclass[12pt,a4paper]{article}
\usepackage{lscape}
\usepackage{ltablex}
\usepackage{siunitx}
\usepackage{ragged2e}
\usepackage{booktabs}
\usepackage{caption}
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{\RaggedRight \renewcommand{\arraystretch}{1.75} \small
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\captionsetup{singlelinecheck=off, justification=centering}
\begin{tabularx}{\linewidth}{p{2.5cm}p{2cm}p{2.5cm}XXX}
\caption{Monolingual Online/Offline Recognition Systems}
\label{Tab:2} \\
\toprule\midrule
Author&
Script&
Text mode&
classifier &
Dataset &
Results \\
\midrule\midrule
\endfirsthead
\multicolumn{6}{c}{{\tablename} \thetable{} -- Continued}\\ \toprule\midrule
Author&
Script&
Text mode&
classifier &
Dataset &
Results \\
\midrule\midrule
\endhead
Rabi et al (2017) & Cursive Arabic & Offline Handwritten & HMM & IFN/ENIT &87.93\% \\
Roy et al (2017) & Arabic & Offline, Handwritten & Deep belief network with HMM & IFN/ENIT & 89.46\% \\
Iwana et al (2017) & Latin & Online & DTW & UNIPEN digits data & 96.67\% \\
Sen et al (2017) & Bangla & Online & & Handwritten 10,000 character dataset & 95.57\% \\
Bhattacharya et al (2016) & Bangla& Online & & 33453 word samples written by 31 writers & 94.3\% \\
Dash et al (2016) & Bangla & Offline & Binary external symmetry axis constellation with Boolean matching & ISI kolkata and IITBBS database for Odia numerals, ISI Kolkata bangle numeral and IITBBS odia character database & 99.35\%, 98.9\%, 99.48\% and 95.01\% respectively accuracy \SI{0.02528}{\s}, \SI{0.02615}{\s}, \SI{0.02392}{\s}, \SI{0.03791}{\s} resptive time \\
Pengchao et al (2016) & Chinese & Printed,offline & CNN & 2789965 samples of GB2312-80 database & 99.90\% \\
Zhang et al (2016) & Chinese & Online & Deep CNN & CASIA-OLHWDB 1.0, CASIA-OLHwDB 1.1 & 98.44\% and 98.05\% \\
Sundram et al (2015) & Tamil & Online & Expert SVMs & 15000 handwritten isolated words & 93.0\% and 81.6\% at symbol and word level respectively. \\
Suyyagh et al (2015) & Arabic & Offline Handwritten & FPGA & IFN/ENIT & 20 times faster than the software implementation and is less accurate by only 2.8\% \\
Arora et al (2014) & Gurumukhi & Offline Printed & SVM with linear and polynomial kernel & 7257 different images & 92.38\% for linear, 85.38\% for polynomial \\
Nguyen et al (2014) & English & Online & SVM with semi incremental approach & IAM-OnDB & 65.70\% WRR\\
Bral et al (2014) & Bangla &Online Handwritten & 5890 training set, 3534 test set & 91.6\% \\
A et al (2014) & Devanagari & Online & HMM with Nearest Neighbor classifier & 32,192 samples of 47 characters & 91.38\% \\
\end{tabularx}
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}
\end{document}
