Deteccio i reconeixement de text mitjancant l'estimacio de l'amplada del trac
This report presents a method for detecting and recognizing text in television pictures, which is based on estimating the stroke width. The method extracts the text that is present in the image, and writes it in a text file, so it can be used together with other features, for image indexing. This work on text detection and recognition for image indexing was developed in the context of the CENIT Buscamedia project. That is why, throughout the project, different versions of software were delivered to Televisio de Catalunya, which is one of the companies working with the project CENIT Buscamedia. A text detector has been developed, starting from a first implementation of the algorithm published by Microsoft Research paper, "Detecting text in natural scenes with stroke width transform" Two new stages have been included, binarization and recognition. For the recognition we have used an open source software developed by Google, the Tesseract OCR. Previously, a binarization stage was used to binarize the output of the text detector and facilitate the task of the OCR system. The results have improved considerably compared with the first version implemented, achieving a rate of 5.67% non detected text versus 15.5% of the initial software. The quality factor has also been improved from an initial 39.03% to a 64.56% Deteccio i reconeixement de text mitjancant l'estimacio de l'amplada del trac Index 1. Introduccio 1 2. Objectius del PFC 3 3. Estat de l'art 4 3.1. Metodes de deteccio de text en imatges 4 3.1.1. Metodes basats en la segmentacio de la imatge 4 3.1.2. Metodes basats en els contorns dels caracters 6 3.1.3. Metodes basats en l'amplada del trac 8 3.2. Metode triat. 9 4. Algorisme 10 4.1. Descripcio general 10 4.2. Deteccio de text 12 4.2.1. Deteccio de contorns 14 4.2.2. SWT 15 4.2.3. Creacio de les components 19 4.2.4. Filtratge de components 21 4.2.5. Creacio de les cadenes 25 4.2.6. Analisis de les cadenes 30 4.3. Binaritzacio 32 4.3.1. Metode triat 32 4.4. Reconeixement de text 34 5. Avaluacio del sistema 36 5.1. Avaluacio segons els criteris ICDAR 36 5.1.1. Resultats obtinguts: 39 5.2. Limitacions de l'algorisme 42 5.3. Comparacio de resultats entre algorisme inicial i final. 45 6. Conclusions 47 6.1. Treball futur 47 7. Referencies 50 Deteccio i reconeixement de text mitjancant l'estimacio de l'amplada del trac 1