A comprehensive survey on word recognition for non-Indic and Indic scripts

被引:0
|
作者
Harmandeep Kaur
Munish Kumar
机构
[1] Maharaja Ranjit Singh Punjab Technical University,Department of Computational Sciences
来源
关键词
Word Recognition; Holistic approach; Non-Indic scripts; Indic scripts;
D O I
暂无
中图分类号
学科分类号
摘要
The term handwriting recognition is used to describe the capability of a computer system to transform human handwriting into machine processable text. Handwriting recognition has many applications in various fields such as bank-cheque processing, postal-address interpretation, document archiving, mail sorting and form processing in administration, insurance offices. A collection of different scripts is employed in writing languages throughout the world. Many researchers have done work for handwriting recognition of various non-Indic and Indic scripts from the most recent couple of years. But, only a limited number of systems are offered for word recognition for these scripts. This paper presents an extensive systematic survey of word recognition techniques. This survey of word recognition is classified broadly based on different scripts in which a word is written. Experimental evaluation of word recognition tools/techniques is presented in this paper. Different databases have been surveyed to evaluate the performance of techniques used to recognize words, and the achieved recognition accuracies have been reported. The efforts in two directions (non-Indic and Indic scripts) are reflected in this paper. We increased awareness of the potential benefits of word recognition techniques and identify the need to develop an efficient word recognition technique. Recommendations are also provided for future research. It is also observed that the research in this area is quietly thin and still more research is to be done, particularly in the case of word recognition of printed/handwritten documents in Indic scripts.
引用
收藏
页码:897 / 929
页数:32
相关论文
共 50 条
  • [1] A comprehensive survey on word recognition for non-Indic and Indic scripts
    Kaur, Harmandeep
    Kumar, Munish
    PATTERN ANALYSIS AND APPLICATIONS, 2018, 21 (04) : 897 - 929
  • [2] Character and numeral recognition for non-Indic and Indic scripts: a survey
    Kumar, Munish
    Jindal, M. K.
    Sharma, R. K.
    Jindal, Simpel Rani
    ARTIFICIAL INTELLIGENCE REVIEW, 2019, 52 (04) : 2235 - 2261
  • [3] Character and numeral recognition for non-Indic and Indic scripts: a survey
    Munish Kumar
    M. K. Jindal
    R. K. Sharma
    Simpel Rani Jindal
    Artificial Intelligence Review, 2019, 52 : 2235 - 2261
  • [4] Online handwriting recognition systems for Indic and non-Indic scripts: a review
    Singh, Harjeet
    Sharma, R. K.
    Singh, V. P.
    ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (02) : 1525 - 1579
  • [5] Online handwriting recognition systems for Indic and non-Indic scripts: a review
    Harjeet Singh
    R. K. Sharma
    V. P. Singh
    Artificial Intelligence Review, 2021, 54 : 1525 - 1579
  • [6] Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey
    Dargan, Shaveta
    Kumar, Munish
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2019, 26 (04) : 1283 - 1311
  • [7] Writer Identification System for Indic and Non-Indic Scripts: State-of-the-Art Survey
    Shaveta Dargan
    Munish Kumar
    Archives of Computational Methods in Engineering, 2019, 26 : 1283 - 1311
  • [8] A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts
    Sukhandeep Kaur
    Seema Bawa
    Ravinder Kumar
    Artificial Intelligence Review, 2020, 53 : 1813 - 1872
  • [9] A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts
    Kaur, Sukhandeep
    Bawa, Seema
    Kumar, Ravinder
    ARTIFICIAL INTELLIGENCE REVIEW, 2020, 53 (03) : 1813 - 1872
  • [10] Cross-language framework for word recognition and spotting of Indic scripts
    Bhunia, Ayan Kumar
    Roy, Partha Pratim
    Mohta, Akash
    Pal, Umapada
    PATTERN RECOGNITION, 2018, 79 : 12 - 31