The　success　of　learning　techniques　in　solving　a　variety　of　hard　AI　problems　promotes　the　flourish　of　recognition-based　applications.　Many　state-of-the-art　text　localization　systems,　which　can　detect　and　report　the　positions　of　text　segments　in　an　image,　are　mainly　implemented　with　learning-based　techniques.　Data-driven　learning　raises　a　series　of　questions　on　how　to　verify,　validate　and　evaluate　such　learning-based　systems.　In　this　paper,　we　propose　a　methodology　to　automatically　evaluate　the　stability　of　text　localization　systems　via　metamorphic　relations,　where　a　stable　system　should　output　consistent　results　for　similar　inputs　with　the　same　text　segments.　We　introduce　six　metamorphic　relations　that　should　be　preserved　in　a　stable　text　localization　system　and　define　the　corresponding　metrics　for　stability　evaluation.　With　the　defined　metamorphic　relations,　we　apply　metamorphic　testing　techniques　to　compare　the　inputs　and　outputs　to　evaluate　system　stability,　and　further　diagnose　the　causes　of　inconsistency.　The　extensive　experimentation　on　both　academic　and　commercial　text　localization　systems　demonstrates　the　effectiveness　of　our　method　on　stability　evaluation　for　such　systems.　(C)　2021　Elsevier　Inc.　All　rights　reserved.