The　benchmarking　database　plays　an　essential　role　in　evaluating　the　performance　of　the　touching　character　string　segmentation　algorithm.　In　this　paper,　we　present　a　new　touching　Tibetan　character　strings　database.　Firstly,　using　the　previous　proposed　layout　analysis　and　text-line　segmentation　algorithms,　we　segment　scanned　images　of　historical　Tibetan　documents　into　textline　images.　Then,　we　find　candidate　touching　Tibetan　character　strings　using　connected　component　analysis　and　screen　out　the　correct　touching　samples.　Finally,　we　annotate　the　data　manually　and　establish　the　touching　character　database.　The　database　contains　5,844　images　of　two-touching　characters　and　1,399　images　of　more　than　two-touching　characters.　It　is　applicable　to　evaluate　the　segmentation　algorithms　for　the　touching　Tibetan　character　strings.　For　each　image,　the　annotated　ground　truth　file　includes　class　labels,　candidate　segment　points,　baseline　and　average　stroke　width　of　a　Tibetan　single　character.　According　to　the　type　of　touching,　we　divide　the　touching　character　string　into　three　types:　AB,　OB　and　BB.　We　also　count　the　number　of　different　type　of　samples　and　find　that　76.27%　of　the　samples　belongs　to　the　third　type　(BB).　In　the　end,　we　measure　the　performance　of　the　over-segmentation　algorithm　on　this　database　for　reference.