Adaptive Hindi OCR using generalized Hausdorff image comparison

Abstract

We present an adaptive Hindi OCR implemented as part of a rapidly retargetable language tool effort. The system includes: script identification, character segmentation, training sample creation, and character recognition. In script identification, Hindi words are identified from bilingual or multilingual documents based on features of the Devanagari script or using Support Vector Machines. Identified words are then segmented into individual characters in the next step, where the composite characters are identified and further segmented based on the structural properties of the script and statistical information. Segmented characters are recognized using generalized Hausdorff image comparison (GHIC) and postprocessing is applied to improve the performance. The OCR system, which was designed and implemented in one month, was applied to a complete Hindi--English bilingual dictionary and a set 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. The presented method can also be extended to design OCR systems for different scripts.