This document summarizes the development of Thai optical character recognition (OCR) using dynamic time warping with font files. The researchers aimed to develop Thai OCR algorithms using font files to recognize characters more easily and accurately compared to traditional OCR methods. They tested the efficiency of dynamic time warping versus Hausdorff distance on different font types and found dynamic time warping had higher accuracy rates while Hausdorff distance was slightly faster. The researchers concluded font files could improve OCR and it would be useful to recognize font types automatically without user input.

1. The Development of Thai-OCR by Using Dynamic Time Warping technique with Font Files

4. Introduction Working diagram of Artificial Neural Network Feature extraction Learning until get satisfying value Target data Input data Adjust weight of feature value Get pattern for analyze real input data (need skill and experience of researchers) (need the large number of data) (waste much time) (limited fonts are allowed)

6. Concept Font files Printed matters Same pattern Easy to recognize

9. Working structure Post Processing Preprocessing Processing Hausdorff Distance Dynamic Time Warping Efficiencies comparison

10. Result Table 1 : Time usage 26.45 29.89 Average 27.67 33.33 Cordia New 30.00 30.67 PS Pimpdeed 21.67 25.67 Angsana New Hausdorff Distance Dynamic Time Warping Time usage (second/1 page) Fonts

11. Result Table 2 : Accuracy 76.19 76.50 Average 75.71 77.07 Cordia New 82.19 71.80 PS Pimpdeed 70.68 80.64 Angsana New Hausdorff Distance Dynamic Time Warping Accuracy (%) Fonts