•    Freeware
  •    Shareware
  •    Research
  •    Localization Tools 20
  •    Publications 669
  •    Validators 2
  •    Mobile Apps 22
  •    Fonts 31
  •    Guidelines/ Draft Standards 3
  •    Documents 13
  •    General Tools 31
  •    NLP Tools 105
  •    Linguistic Resources 233

Search Results | Total Results found :   1129

You refine search by : All Results
  Catalogue
A reasonably large database of online handwritten Bangla characters has been developed. Such a handwritten character sample is composed of one or more strokes. Seventy five such stroke classes have been identified on the basis of the varying handwriting styles present in the character database. Each character sample is a sequence of strokes emanating from these stroke classes. Another database of handwritten Bangla strokes has been developed from the character database. This is the first such database for Bangla script. Certain stroke level features are defined on the basis of certain extremum points which represent the stroke shape reasonably well.

Added on September 5, 2017

0

  More Details
  • Contributed by : OHWR consortium
  • Product Type : Research Paper
  • License Type :
  • System Requirement :
  • Author : Chandan Biswas,Ujjwal Bhattacharya,Swapan Kumar Parui
Author Community Profile :

This work describes the development of Assamese online numeral recognition system using Hidden Markov Models (HMM) and Support Vector Machines (SVM). Preprocessed (x,y) coordinates and their first and second derivatives at each point are used as features for both the modeling techniques. The two systems are developed individually using HMM and SVM. The results from both the systems are then combined using two different approaches. In the first approach, the scores from both the classifiers are directly merged and an improvement in performance is observed in the combined system (comb−1). In the second approach,the confusion patterns from HMM and SVM classifiers are also analyzed. Based on this, the results are further combined to obtain a final hybrid numeral recognizer with anenhanced performance (comb−2). The HMM, SVM, Comb-1and Comb-2 systems provide average recognition performance of 96.5, 96.8, 98 and 98.3, respectively.

Added on September 5, 2017

0

  More Details
  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
  • License Type :
  • System Requirement :
  • Author : Bandita Sarma,R. Krishna Naik,S.R.M.Prasanna,Chitralekha Mahanta,Kapil Mehrotra,Swapnil Belhe

A frequency count based two stage classification approach is proposed by combining generative and discriminative modeling principles for online handwritten character recognition.The first stage classifier based on Hidden Markov Model (HMM) returns top-K ranking characters out of the total N classes. In the second stage, pairwise classifiers for K (K−1) /2 unique combinations of top-K characters using Support Vector Machine (SVM) are developed. Usually pairwise classifiers are trained for most confused character pairs, by analyzing the confusion matrix.

Added on September 5, 2017

0

  More Details
  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
  • License Type :
  • System Requirement :
  • Author : Subhasis Mandal,Himakshi Choudhury ,S. R. Mahadeva Prasanna,Suresh Sundaram

Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This work proposes a curvature point detection based method to predict variable number of states for modeling a handwritten stroke. The proposed method selects appropriate points from a trace so that the portion between two consecutive points is modeled as an HMM state. Accordingly, based upon handwritten stroke shape complexity, the number of appropriate points selected will change and hence the number of states assigned to the corresponding stroke. In the proposed method, the number of states is proportional to the shape complexity of the given stroke as opposed to fixed in case of brute-force. The HMM based stroke recognizer consisting of 181 distinct strokes, was trained on a set of 52,977 examples collected from approximately 100 native Assamese writers. The evaluation was done on 43,828 examples collected from same users in different sessions. The experimental results demonstrate the benefits of the proposed technique over the brute-force method, especially in case of complex shape strokes.

Added on September 4, 2017

3

  More Details
  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
  • License Type :
  • System Requirement :
  • Author : Subhasis Mandal,S. R. Mahadeva Prasanna,Suresh Sundaram

Hidden Markov Models (HMM) are the widely used modeling techniques for online handwriting recognition. This paper describes both stroke based and character based methods for Assamese handwritten character recognition using HMM classifier. In stroke based method, unique strokes that are used to write the characters are grouped and then HMM modeling is done for each of these selected class of strokes. A character can comprise of one or multiple strokes. Reference set is prepared by analyzing the different combinations of strokes and the degree of confusion between similar strokes. The stroke based method comprises of two stages. First, the stroke sequences in the test character is recognized by stroke based HMM classifier and in the second stage this sequence of strokes is compared against the entries of the reference set. The character corresponding to the matched stroke sequence in the reference set is considered as the recognized character. In character based method, each character as a whole is modeled using HMM and the classifier directly predicts the character class. Experiments were performed on 141 Assamese characters, collected from 100 native Assamese writers and it is observed that character based method gives better result than stroke based method.

Added on September 4, 2017

0

  More Details
  • Contributed by : OHWR Consortium
  • Product Type : Research Paper
  • License Type :
  • System Requirement :
  • Author : Himakshi Choudhury ,Subhasis Mandal, Sanjeevan Devnath,S. R. M. Prasanna,S. Sundaram