diff --git a/tensorflow/lite/g3doc/guide/android.md b/tensorflow/lite/g3doc/guide/android.md
index ba1bc46e8b4..2c148ecbe7d 100644
--- a/tensorflow/lite/g3doc/guide/android.md
+++ b/tensorflow/lite/g3doc/guide/android.md
@@ -270,9 +270,10 @@ There are two ways to use TFLite through C++ if you build your app with the NDK:
 
 This is the *recommended* approach. Download the
 [TensorFlow Lite AAR hosted at JCenter](https://bintray.com/google/tensorflow/tensorflow-lite),
-rename it to `tensorflow-lite-*.zip`, and unzip it. You must include the three
-header files in `headers/tensorflow/lite/c/` folder and the relevant
-`libtensorflowlite_jni.so` dynamic library in `jni/` folder in your NDK project.
+rename it to `tensorflow-lite-*.zip`, and unzip it. You must include the four
+header files in `headers/tensorflow/lite/` and `headers/tensorflow/lite/c/`
+folder and the relevant `libtensorflowlite_jni.so` dynamic library in `jni/`
+folder in your NDK project.
 
 The `c_api.h` header file contains basic documentation about using the TFLite C
 API.
@@ -297,5 +298,5 @@ bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so
 Currently, there is no straightforward way to extract all header files needed,
 so you must include all header files in `tensorflow/lite/` from the TensorFlow
 repository. Additionally, you will need header files from
-[FlatBUffers](https://github.com/google/flatbuffers) and
+[FlatBuffers](https://github.com/google/flatbuffers) and
 [Abseil](https://github.com/abseil/abseil-cpp).