Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%

Efficient multiscale and multifont optical character recognition system based on robust feature description

International audienceOptical Character Recognition (OCR) is the process of translating images of text into a comprehensible machine format. Generally, an OCR system is composed of binariza-tion, segmentation and recognition stages. Given an extracted binary character, the recognition stage ensures its description and decides its corresponding ASCII code. In this paper, we propose a new OCR system that aims to high speed, Multiscale and Multifont character recognition. Our proposal is based essentially on robust description using a new Unified Character Descriptor (UCD). In addition, a character type-face and font-size recognition is performed to choose the adequate template for faster matching process. Obtained OCR Accuracy of our proposed System is 1.5x higher then that reached by Tesseract on the LRDE dataset