Datasets:
deepcopy
/
Azu-Handwritten-Mathematical-Expression

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