Somuncu, EbruAtasoy, Nesrin Aydin2024-09-292024-09-2920221300-18841304-4915https://doi.org/10.17341/gazimmfd.866552https://search.trdizin.gov.tr/tr/yayin/detay/1064187https://hdl.handle.net/20.500.14619/6873In the literature, effective and common technology used for in door navigation has not been found. For the solution of the problem, recognition of the text images used indoors and determination of the current location according to the images seems to be the most feasible solution. In this study, deep learning methods, which are used successfully in many fields, were used to recognize signage images used in interior spaces. The created model extracts high-level features such as corners and edges in the images with the Convolutional Neural Network. Next, the sequencing properties of the letters are preserved with the Recurrent Neural Network-Based Bidirectional Long-Short-Term Memory. The Connectionist Temporal Classification approach is used to solve the repetitions and other problems in the sequence. Mistakes in words created with the characters generated by the trained model are improved using Levenshtein distance. The success of the model in the tests performed with the Synth90k data set was 96%, and the success in Turkish images with the same character set of the model trained with the Sytnh90kwas 93%. The results obtained demonstrate the success of the proposed approach.trinfo:eu-repo/semantics/openAccessCharacter recognitionconvolutional recurrent neural networkdeep learninglevenshtein distancerecurrent neural networkArticle10.17341/gazimmfd.8665522-s2.0-85119936444271Q217106418737WOS:000719285900001Q4