Translate first paragraph of background.

recognize_digits/README.en.md

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 # Recognize Digits
 
-Source code of this chapter is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits)， For the first-time use, please refer to PaddlePaddle[installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。
+Source code of this chapter is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits)， For the first-time use, please refer to PaddlePaddle [installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。
 
 ## 背景介绍
 当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。MNIST数据集作为一个简单的计算机视觉数据集，包含一系列如图1所示的手写数字图片和对应的标签。图片是28x28的像素矩阵，标签则对应着0~9的10个数字。每张图片都经过了大小归一化和居中处理。
 
+## Background
+When we study programming, the first program is usually printing “Hello World.” In Machine Learning, or Deep Learning, this is hand-written digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains hand-written digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered.
+
 <p align="center">
 <img src="image/mnist_example_image.png" width="400"><br/>
 图1. MNIST图片示例