Datasets:
deepcopy
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Azu-Handwritten-Mathematical-Expression

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\lim _ { n \rightarrow \infty } x _ { n } = L
train
\int \cos t d t = \sin t
train
( ( 1 8 / 6 ) \times 1 6 6 ) - ( 8 5 - 1 5 8 ) = 5 7 1
train
\alpha + \beta = \beta + \alpha
2014
\sum _ { i = 1 } ^ { n } e ^ { - \theta _ { i } } \sum _ { i = 1 } ^ { n } e ^ { + \theta _ { i } }
2019
\tan z = \frac { \sin z } { \cos z }
train
\frac { 1 8 \div 6 } { 2 4 \div 6 } = \frac { 3 } { 4 }
2014
x _ { 2 } = x \sin \theta
2016
1 3 + \pi r ^ { 2 }
train
5
train
b = c \sin B = c \cos A
train
\frac { d } { d t } P ( t ) = P ( t ) \cdot ( 1 - P ( t ) )
train
2 4 + 8 + 6 , 1 6 + 1 6 + 6 , 1 6 + 8 + 8 + 6 , 1 2 + 1 2 + 8 + 6 , 1 2 + 8 + 6 + 6 + 6
2019
F _ { n } = F _ { n - 1 } + F _ { n - 2 }
train
\int \limits _ { 0 } ^ { R } \frac { 2 x d x } { 1 + x ^ { 2 } } = \log ( 1 + R ^ { 2 } )
train
- \sum 0
train
\log _ { 2 } \frac { 1 } { 2 } + \log _ { 4 } \frac { 2 } { 4 }
train
| a + i b | = \sqrt { x ^ { 2 } + y ^ { 2 } }
train
1 0 3 \pm 1 4 4 + 1 1 3 + 2 7
train
3 - ( ( 3 7 \div 1 5 ) - ( 1 8 9 - 1 1 7 ) ) \geq 7 2
train
( z ^ { \frac { n } { 2 } } + y ^ { \frac { n } { 2 } } ) ( z ^ { \frac { n } { 2 } } - y ^ { \frac { n } { 2 } } ) = x
train
b + \pi ^ { X }
train
\frac { 3 x + y } { z } = ( \frac { A - 1 } { x ^ { 2 } + y ^ { 2 } } )
train
A = A ^ { x } e _ { x } + A ^ { y } e _ { y }
2019
a _ { n + 2 } = a _ { n } - q _ { n + 2 } a _ { n + 1 }
train
7 7
train
v = v _ { 0 } t - g t ^ { 2 }
train
\sin ( \frac { \pi } { 3 } ) = \frac { 1 } { 2 }
train
n \geq 8
2016
+ 7 8
2019
\lim _ { x \rightarrow 1 } p ( x ) B _ { n } ( x )
2016
\sqrt { 1 + \sqrt { 2 + \sqrt { 3 + \sqrt { 4 } } } }
train
c ^ { 2 } = a ^ { 2 } + b ^ { 2 } - 2 a b \cos C
train
k ^ { X }
train
\cos \theta = \frac { x } { \sqrt { x ^ { 2 } + y ^ { 2 } } }
train
a + \sqrt { \frac { b + c } { d + f } }
train
h ( t )
train
x = \frac { x _ { 1 } + x _ { 2 } } { 2 }
2016
[ a b ] = \frac { 1 } { 2 } ( a b - b a )
2019
i ^ { 2 }
train
p = \sqrt { a ^ { 2 } + b ^ { 2 } - 2 a b \cos A }
train
S _ { n } = \sum _ { k = 1 } ^ { n } a _ { k }
train
E A c
train
\phi n
train
\tan ( 3 x ) = \frac { 3 \tan ( x ) - \tan ^ { 3 } ( x ) } { 1 - 3 \tan ^ { 2 } ( x ) }
2014
6 9 \times 1 5 = 1 0 3 5
train
\frac { 1 } { \frac { 1 } { 2 } ( \frac { 1 } { a } + \frac { 1 } { b } ) } = \frac { 2 a b } { a + b }
train
\lim _ { n \rightarrow \infty } ( \sqrt { n + 2 } - \sqrt { n } ) = 0
train
\sin ( \frac { \pi } { 3 } ) = \frac { 1 } { 2 }
train
( y - 3 ) ( y + 3 ) + ( y + 3 ) = 0
train
0 5 C
train
\frac { 2 7 } { 7 }
2019
2 ^ { 5 } + 2 + 1
train
y = \tan ( \theta / 3 )
2019
7 X
train
b ^ { 3 } = \frac { 5 } { 5 4 }
train
\lim _ { x \rightarrow \frac { 1 } { 4 } } \frac { 1 - 4 ^ { x - \frac { 1 } { 4 } } } { 1 - 4 x }
train
x < \frac { c - b } { a }
train
g ( 2 , 3 , 4 ) = 3 4 ^ { - 3 }
train
2 ^ { n } \times 2 ^ { n }
train
x _ { 1 } \times x _ { 2 } \times x _ { 3 } \times x _ { 4 } = X
train
7 7 / 1 6 7 \leq 0
train
y ^ { 2 } \pm \sqrt { u - p } ( y - \frac { q } { 2 ( u - p ) } ) + \frac { u } { 2 } = 0
train
\beta = ( \cos ^ { 4 } \theta + A \sin ^ { 4 } \theta ) ^ { \frac { 1 } { 2 } }
2016
v
train
V = a \cdot b \cdot c
train
\frac { 1 1 } { 3 } \sqrt { 3 }
2014
u _ { 1 } = \beta u _ { 2 }
train
8 9 ( 1 9 6 1 ) 9
2019
\pi
train
o _ { X }
train
\frac { a } { b } = c
train
( 1 . 6 5 5 , 1 4 . 4 4 7 , 3 . 3 9 8 )
2019
\tan ( \frac { \pi } { 4 } ) = 1
train
\sqrt { \frac { c } { N } }
train
- v + \frac { L } { j }
train
1 2 0 - 1 4 - 1 0 7 \leq 2 1 3
train
y ( x + k p ) = ( x + k p ) ^ { 2 } - n
train
a _ { 1 } + a _ { 2 }
train
Y \sum B
train
2 ^ { d } \alpha
train
1 + 3 7
train
b y a
2019
\int ( 2 ^ { x } - 3 e ^ { x } ) d x
train
\frac { \sin B + \sin C } { \cos B + \cos C }
train
1 4 3 \div 1 7 8 \geq 0
train
\tan \gamma _ { i }
2014
X \neq F
train
r = z \tan { \alpha }
2016
9 + 2
2014
\frac { x } { a + \frac { x } { b - \frac { x } { c } } }
train
( L - j )
train
\sum _ { i = 1 } ^ { k } \frac { x ^ { a _ { i } } } { 1 - x ^ { b _ { i } } } = \frac { 1 } { 1 - x }
train
\int _ { - \infty } ^ { \infty } d x ^ { 1 }
2016
( x , w )
train
d L _ { x _ { \mu } }
train
\frac \pi 2 + n \pi
2019
( 5 5 - 1 4 0 - 1 6 8 ) \times ( ( 7 6 \div 7 7 ) - ( 1 6 2 \times 6 8 ) ) \neq - 1 1 6 4 3 5
train
\{ a \}
2016
\frac { 2 \tan \alpha } { 1 - \tan ^ { 2 } \alpha }
train