[ca] En els darrers anys, hi ha hagut una tend encia creixent dintre del DL en la detecci o d'objectes. Cada cop m es sorgeixen m es i millors models per a poder fer una detecci o m es precisa. Dintre d'aquest ambit, tamb e hi ha una tend encia que va agafant cada cop m es for ca. Es tracta de la detecci o de text i el seu reconeixement a posteriori per a que tasques tedioses ens resultin m es senzilles. En el treball que es presenta a continuaci o, el qual es una part de un projecte de majors dimensions, es fa us d'aquestes t ecniques per a poder realitzar una tasca molt concreta com es la de poder detectar i recon eixer noms de comer cos. Aquesta tasca facilitaria molt el treball de les administracions p ubliques ja que els hi resultaria m es f acil saber si diferents comer cos consten a les bases de de dades i si a m es segueixen actius. Per aix o, pretenen tenir un sistema que amb imatges que es capturin a trav es de c ameres col locades en vehicles p ublics es pugui detectar i recon eixer aquests establiments i buscar-los en les seves bases de dades per a poder tenir la informaci o actualitzada. Tot i que aquest treball es basa en una prova de concepte, ja que no sabem la viabilitat d'aquest projecte. es pret en fer un sistema que a partir d'imatges pr eviament capturades, sigui capa c de decetar el text de la imatge, saber quins car acters hi ha en el text i comparar-lo amb el text anotat. Per no tar-se molt i poder tenir v aries perspectives, s'han utilitzat models diferents per a la detecci o i tamb e per al reconeixement, a m es de la corresponent comparaci o dintre d'una mateixa t ecnica por a poder saber avantatges i inconvenients que ens ofereixen. Adicionalment. tamb e s'ha estudiat la possibilitat de fer us de plataformes externes per a la realitzaci o d'aquesta tasca com APIs de grans empreses (Google, Amazon i Microsoft), utilitzar m etodes cl assics de OCR o fer us de sistemes E2E. 2

[en] In recent years, there has been a growing trend inside Deep Learning (DL) in object detection. Every time more and better models are emerging to make a detection with more precision. Within this area, there also exists a trend that is getting more force. It consists of Scene Text Detection (STD) and Scene Text Recognition (STR) so that tedious tasks can be easier. In the work presented below, which is a part of a bigger project, these techniques are used to perform a concrete task which is to be able to detect and recognize names of stores. This task would greatly help the work of public administrations since it would be easier to know if di erent stores are in the databases and if they are still active. For this, they aim to have a system with images that are captured through cameras placed in public vehicles. The system can detect and recognize these establishments and look for them in their databases to have updated information. Although this work is based on a proof of concept, since we do not know the feasibility of the said project, it is intended to make a system that, from previously captured images, is capable of detecting the text in the image, knowing which characters appear and compare it with the hand labelled text. In order to not limit the extension too much and to have multiple points of view, di erent models have been used for detection and also for recognition, furthermore to the corresponding comparison within the same technique to know the advantages and disadvantages they o er. In addition, other possibilities have also been considered such as using external platforms to perform this task, such as APIs from big companies (Google, Amazon, Microsoft), using classic methods of Optical Character Recognition (OCR) or using End-to-End (E2E) systems.