  Spectral type recognition is an important and fundamental step of large sky
survey projects in the data reduction for further scientific research, like
parameter measurement and statistic work. It tends out to be a huge job to
manually inspect the low quality spectra produced from massive spectroscopic
survey, where the automatic pipeline may not provide confident type
classification results. In order to improve the efficiency and effectiveness of
spectral classification, we develop a semi-automated toolkit named ASERA, A
Spectrum Eye Recognition Assistant. The main purpose of ASERA is to help the
user in quasar spectral recognition and redshift measurement. Furthermore it
can also be used to recognize various types of spectra of stars, galaxies and
AGNs (Active Galactic Nucleus). It is an interactive software allowing the user
to visualize observed spectra, superimpose template spectra from the Sloan
Digital Sky Survey (SDSS), and interactively access related spectral line
information. It is an efficient and user-friendly toolkit for accurate
classification of spectra observed by LAMOST (the Large Sky Area Multi-object
Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java
standalone application and a Java applet. ASERA has a few functions, such as
wavelength and flux scale setting, zoom in and out, redshift estimation,
spectral line identification, which helps user to improve the spectral
classification accuracy especially for low quality spectra and reduce the labor
of eyeball check. The function and performance of this tool is displayed
through the recognition of several quasar spectra and a late type stellar
spectrum from the LAMOST Pilot survey. Its future expansion capabilities are
discussed.
