As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques.
#Kannada barakhadi pdf Offline
Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The survey is organized into different sections. A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The proposed technique produces an average recognition rate of 84.56% using SVM and 74.47% using ANN. Then, the classification result was measured for SVM and Artificial Neural Network (ANN) based classifiers on self-generated training and testing data sets which contain 2500 different samples of 50 characters in the Bengali character-set. Most importantly, the geometry based feature extraction method has been employed to extract the effective features from the Bengali characters for the classification purposes. In this paper, different image processing steps are used including image acquisition, digitization, preprocessing, segmentation and feature extraction for tackling the difficulty. This proposed approach for identifying Bengali characters is based on character geometry-oriented feature extraction for different handwritten characters. Bearing in mind the complexity of the problem, an efficient approach for recognizing handwritten Bengali alphabet is proposed in this work. In addition, among the huge amount of complex shaped characters, some are very similar in shape those possess severe difficulty to recognize handwritten Bengali characters.
![kannada barakhadi pdf kannada barakhadi pdf](https://img3.exportersindia.com/product_images/bc-full/dir_112/3332377/reusable-hindi-writing-practice-note-1533152.jpg)
Moreover, the ambiguity and precision error are common in handwritten words.
![kannada barakhadi pdf kannada barakhadi pdf](https://images-na.ssl-images-amazon.com/images/I/81JLn2X2wxL._SL1500_.jpg)
There are 50 complex shaped characters in Bengali alphabet set and working with this huge amount of characters with an appropriate set of feature is a tough problem to solve.
![kannada barakhadi pdf kannada barakhadi pdf](https://www.formsbirds.com/formimg/kannada-alphabet-chart/3230/kannada-letters-d1.png)
Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script is the massive amount of characters in Bengali language and their complex shapes.