pd.read_json ignores 'category' dtypes
#### Code Sample, a copy-pastable example if possible

```python
dtypes_dict = {'interactionRelevance': 'uint8', 'productBrand': 'category', ...}
data_frame = pd.read_json(input_file, lines=True, dtype=dtypes_dict)
```
#### Problem description

Category type is just ignored, the columns specified to be 'category' from the Json just becomes 'object' with the related memory consumption.

Debugging I noticed that  in /usr/local/lib/python3.6/site-packages/pandas/io/json/json.py:677

```python
                if dtype is not None:
                    try:
                        dtype = np.dtype(dtype)
                        return data.astype(dtype), True
                    except (TypeError, ValueError):
                        return data, False
```
np.dtype('category') doesn't seem correct.
Is this just a bug or I misunderstood something ?


#### Expected Output

#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 17.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.23.3
pytest: None
pip: 10.0.1
setuptools: 39.2.0
Cython: None
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>