Adaptive Hindi OCRUsing Generalized Hausdorff Image Comparison

In this paper, we present an adaptive Hindi OCR using generalized Hausdor image comparison implemented as part of a rapidly retargetable language tool effort. The system includes: script identification, character segmentation, training sample creation and character recognition. The OCR design (completed in one month) was applied to a complete Hindi-English bilingual dictionary (with 1083 pages) and a collection of ideal images extracted from Hindi documents in PDF format. Experimental results show the recognition accuracy can reach 88% for noisy images and 95% for ideal images, both at the character level. The presented method can also be extended to design OCR systems for different scripts.