%0 Thesis %1 teodoro2015sistema %A Teodoro, Beatriz Tomazela %C São Paulo %D 2015 %I Universidade de São Paulo (USP) %K brazilian-sign-language bsl human-skin-segmentation image-processing libras real sign-language-recognition slr %R 10.11606/d.100.2016.tde-20122015-212746 %T Sistema de Reconhecimento Automático de Língua Brasileira de Sinais %U http://www.teses.usp.br/teses/disponiveis/100/100131/tde-20122015-212746 %X The recognition of sign language is an important research area that aims to mitigate the obstacles in the daily lives of people who are deaf and/or hard of hearing and increase their integration in the majority hearing society in which we live. Based on this, this dissertation proposes the development of an information system for automatic recognition of Brazilian Sign Language (BSL), which aims to simplify the communication between deaf talking in BSL and listeners who do not know this sign language. The recognition is accomplished through the processing of digital image sequences (videos) of people communicating in BSL without the use of colored gloves and/or data gloves and sensors or the requirement of high quality recordings in laboratories with controlled environments focusing on signals using only the hands. Given the great difficulty of setting up a system for this purpose, an approach divided in several stages was used. It considers that all stages of the proposed system are contributions for future works of sign recognition area, and can contribute to other types of works involving image processing, human skin segmentation, object tracking, among others. To achieve this purpose we developed a tool to segment sequences of images related to BSL and a tool for identifying dynamic signals in the sequences of images related to the BSL and translate them into portuguese. Moreover, it was also built an image bank of 30 basic words chosen by a BSL expert without the use of colored gloves, laboratory-controlled environments and/or making of the dress of individuals who performed the signs. The segmentation algorithm implemented and used in this study had a average accuracy rate of 99.02% and an overlap of 0.61, from a set of 180 preprocessed frames extracted from 18 videos recorded for the construction of database. The segmentation algorithm was able to target more than 70% of the samples. Regarding the accuracy for recognizing words, the proposed system reached 100% accuracy to recognize the 422 samples from the database constructed (the ones that were segmented), using a combination of the edit distance technique and a voting scheme with a binary classifier to carry out the recognition, thus reaching the purpose proposed in this work successfully.

@mastersthesis{teodoro2015sistema, abstract = {The recognition of sign language is an important research area that aims to mitigate the obstacles in the daily lives of people who are deaf and/or hard of hearing and increase their integration in the majority hearing society in which we live. Based on this, this dissertation proposes the development of an information system for automatic recognition of Brazilian Sign Language (BSL), which aims to simplify the communication between deaf talking in BSL and listeners who do not know this sign language. The recognition is accomplished through the processing of digital image sequences (videos) of people communicating in BSL without the use of colored gloves and/or data gloves and sensors or the requirement of high quality recordings in laboratories with controlled environments focusing on signals using only the hands. Given the great difficulty of setting up a system for this purpose, an approach divided in several stages was used. It considers that all stages of the proposed system are contributions for future works of sign recognition area, and can contribute to other types of works involving image processing, human skin segmentation, object tracking, among others. To achieve this purpose we developed a tool to segment sequences of images related to BSL and a tool for identifying dynamic signals in the sequences of images related to the BSL and translate them into portuguese. Moreover, it was also built an image bank of 30 basic words chosen by a BSL expert without the use of colored gloves, laboratory-controlled environments and/or making of the dress of individuals who performed the signs. The segmentation algorithm implemented and used in this study had a average accuracy rate of 99.02% and an overlap of 0.61, from a set of 180 preprocessed frames extracted from 18 videos recorded for the construction of database. The segmentation algorithm was able to target more than 70% of the samples. Regarding the accuracy for recognizing words, the proposed system reached 100% accuracy to recognize the 422 samples from the database constructed (the ones that were segmented), using a combination of the edit distance technique and a voting scheme with a binary classifier to carry out the recognition, thus reaching the purpose proposed in this work successfully.}, added-at = {2019-09-13T15:31:34.000+0200}, address = {São Paulo}, author = {Teodoro, Beatriz Tomazela}, biburl = {https://www.bibsonomy.org/bibtex/2cc84f91c69fbbef280b02293ed9d39c7/jpmor}, doi = {10.11606/d.100.2016.tde-20122015-212746}, interhash = {32540f020b26372e2774e4cd1e06ebcd}, intrahash = {cc84f91c69fbbef280b02293ed9d39c7}, keywords = {brazilian-sign-language bsl human-skin-segmentation image-processing libras real sign-language-recognition slr}, publisher = {Universidade de São Paulo (USP)}, school = {Universidade de São Paulo (USP)}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Sistema de Reconhecimento Automático de Língua Brasileira de Sinais}, url = {http://www.teses.usp.br/teses/disponiveis/100/100131/tde-20122015-212746}, year = 2015 }