Partitioning and searching dictionary for correction of optically read Devanagari character strings

Abstract. This paper describes a method for the correction of optically read Devanagari character strings using a Hindi word dictionary. The word dictionary is partitioned in order to reduce the search space besides preventing forced matching to an incorrect word. The dictionary partitioning strategy takes into account the underlying OCR process. The dictionary words at the top level have been divided into two partitions, namely: a short-words partition and the remaining words partition. The short-word partition is sub-partitioned using the envelope information of the words. The envelope consists of the number of top, lower, core modifiers along with the number of core charactersp. Devanagari characters are written in three strips. Most of the characters referred to as core characters are written in the middle strip. The remaining words are further partitioned using tags. A tag is a string of fixed length associated with each partition. The correction process uses a distance matrix for a assigning penalty for a mismatch. The distance matrix is based on the information about errors that the classification process is known to make and the confidence figure that the classification process associates with its output. An improvement of approximately 20% in recognition performance is obtained. For a short word, 590 words are searched on average from 14 sub-partitions of the short-words partition before an exact match is found. The average number of partitions and the average number of words increase to 20 and 1585, respectively, when an exact match is not found. For tag-based partitions, on an average, 100 words from 30 partitions are compared when either an exact match is found or a word within the preset threshold distance is found. If an exact match or a match within a preset threshold is not found, the average number of partitions becomes 75 and 450 words on an average are compared. To the best of our knowledge this is the first work on the use of a Hindi word dictionary for OCR post-processing.