Conference Paper

Database Development and Recognition of Handwritten Devanagari Legal Amount Words

Dept. of IT, PICT, Pune, India
DOI: 10.1109/ICDAR.2011.69 Conference: 2011 International Conference on Document Analysis and Recognition, ICDAR 2011, Beijing, China, September 18-21, 2011
Source: IEEE Xplore


A dataset containing 26,720 handwritten legal amount words written in Hindi and Marathi languages (Devanagari script) is presented in this paper along with a training-free technique to recognize such handwritten legal amounts present on Indian bank cheques. The recognition of handwritten legal amount words in Hindi and Marathi languages is a challenging because of the similar size and shape of many words in the lexicon. Moreover, many words have same suffixes or prefixes. The recognition technique proposed is a combination of two approaches. The first approach is based on gradient, structural and cavity (GSC) features along with a binary vector matching (BVM) technique. The second approach is based on vertical projection profile (VPP) feature and dynamic time warping (DTW). A number of highly matched words in both the approaches are considered for the recognition step in the combined approach based on a ranking scheme. Syntactical knowledge related to the languages is also used to achieve higher reliability. To the best of our knowledge, this is the first work of its kind in recognizing handwritten legal amounts written in Hindi and Marathi. Researchers interested in the dataset can contact the authors to get it through a shared link.

Download full-text


Available from: Satish Kolhe
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The discussion in the paper is regarding to the recognition of handwritten Devanagari vowels by means of a classifier named as K-NN (K-Nearest Neighbour). Before applying classifier, feature extortion is accomplished for extracting the feature points (FP) i.e. also known as division points (DP). In this paper the feature extortion is perform through recursive sub division technique, which is first time implemented on Devanagari vowels. K-NN classifier is functioned for the learning and the testing phases, through which the recognition go ahead to the high performances in terms of recognition rate, pre-processing and classification speed. Authors tested the described approach using the ISI (Indian Statistical Institute), Kolkata"s handwritten Devanagari vowels database containing 9191 samples, which is divided into 1:3 as testing and training samples respectively. In the recognition process using K-NN classifier 88 vowels are total wrongly identified out of 2281vowels. The recognition rate comes out to be 96.14%.
    Full-text · Article · Jul 2012 · International Journal of Computer Applications
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A Dissertation submitted in partial fulfilment for the award of the Degree of Master of Technology in department of Computer Engineering
    Full-text · Thesis · Aug 2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Keyword spotting aims to retrieve all instances of a given keyword from a document in any language. In this paper, we propose a novel script independent line based word spotting framework for offline handwritten documents based on Hidden Markov Models. The methodology simulates the keywords in model space as a sequence of character models and uses the filler models for better representation of background or non-keyword text. We propose a two stage spotting framework where the candidate keywords are further pruned using the character based background and lexicon based background model. The system deals with large vocabulary without the need for word or character segmentation. The system has been evaluated on many public dataset from several languages such as IAM for English, AMA for Arabic and LAW for Devanagari. The system outperforms the modern line based approach on the English, Arabic and Devanagari Datasets.
    Full-text · Conference Paper · Sep 2012
Show more