Transform based approach for Indic script identification from handwritten document images

In a multi-script country like India script identification from document images is an essential step before choosing appropriate script specific OCR (Optical Character Recognizer). The problem of handwritten script identification is more challenging compared to printed one due to uneven variations with respect to writers, time, content etc. Increasing efforts are coming day by day from document image processing researchers to develop standard techniques for Indic script identification. But most of the works is found to be considering printed script document images. In this paper a simple, robust and segmentation free technique based on different image transform methods and statistical features to identify any one of the four popular Indic scripts namely Bangla, Roman, Devanagari and Oriya is proposed. A dataset of total 101 handwritten document images comprising of more than 11000 words and 1300 lines with almost equal distribution of each type of scripts are built, which were collected from different writers with varying age, sex and educational qualification. On experimentation, an average accuracy rate of 88.1% is found for Four-scripts combination by MLP (Multilayer Perceptron) classifier after five fold cross validation. The average Tri-Scripts and Bi-Scripts accuracy are found to be 89.7% and 94.3% respectively. The outcome of this work is really impressive considering inherent complexities of handwritten Indic scripts.