The　reconstruction　of　cross-cut　shredded　text　documents　(RCCSTD)　is　an　important　problem　in　forensics　and　is　a　real,　complex　and　notable　issue　for　information　security　and　judicial　investigations.　It　can　be　considered　a　special　kind　of　greedy　square　jigsaw　puzzle　and　has　attracted　the　attention　of　many　researchers.　Clustering　fragments　into　several　rows　is　a　crucial　and　difficult　step　in　RCCSTD.　However,　existing　approaches　achieve　low　clustering　accuracy.　This　paper　therefore　proposes　a　new　clustering　algorithm　based　on　horizontal　projection　and　a　constrained　seed　K-means　algorithm　to　improve　the　clustering　accuracy.　The　constrained　seed　K-means　algorithm　draws　upon　expert　knowledge　and　has　the　following　characteristics:　1)　the　first　fragment　in　each　row　is　easy　to　distinguish　and　the　unidimensional　signals　that　are　extracted　from　the　first　fragment　can　be　used　as　the　initial　clustering　center:　2)　two　or　more　prior　fragments　cannot　be　clustered　together.　To　improve　the　splicing　accuracy　in　the　rows,　a　penalty　coefficient　is　added　to　a　traditional　cost　function.　Experiments　were　carried　out　on　10　text　documents.　The　accuracy　of　the　clustering　algorithm　was　99.1%　and　the　overall　splicing　accuracy　was　91.0%,　according　to　our　measurements.　The　algorithm　was　compared　with　two　other　approaches　and　was　found　to　offer　significantly　improved　performance　in　terms　of　clustering　accuracy.　Our　approach　obtained　the　best　results　of　RCCSTD　problem　based　on　our　experiment　results.　Moreover,　a　more　complex　and　real　problem　-　reconstruction　of　cross-cut　shredded　dual　text　documents　(RCCSDTD)　problem　-　was　tried　to　solve.　The　satisfactory　results　for　RCCSDTD　problems　in　some　cases　were　obtained,　to　authors＇　best　knowledge,　our　method　is　the　first　feasible　approach　for　RCCSDTD　problem.　On　the　other　hand,　the　developed　system　is　fundamentally　an　expert　system　that　is　being　specifically　applied　to　solve　RCCSTD　problems.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.