Empirical Evaluation of Approaches for Digit Recognition

• Main Author: Joosep, Henno
• Format: Others
• Language: English
• Published: Linnéuniversitetet, Institutionen för datavetenskap (DV) 2015
• Subjects: Optical Character Recognition; OCR; Artificial Neural Network; ANN; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-46676

Optical Character Recognition (OCR) is a well studied subject involving variousapplication areas. OCR results in various limited problem areas are promising,however building highly accurate OCR application is still problematic in practice.This thesis discusses the problem of recognizing and confirming Bingo lottery numbersfrom a real lottery field, and a prototype for Android phone is implementedand evaluated. An OCR library Tesseract and two Artificial Neural Network (ANN)approaches are compared in an experiment and discussed. The results show thattraining a neural network for each number gives slightly higher results than Tesseract.