Handwritten Hindi Character Recognition

Abstract

Devanagari, the most accepted script in India and Hindi is the only dialect which is widely spoken and written, so Handwritten Hindi character Recognition is done. Optical Character Recognition (OCR) is used for pattern recognition, it can be online or offline. Handwritten text is electronically converted into machine learning language. Handwritten character Recognition has many applications like cheque reader,passport reader,address reader,specific tasks readers. Devanagari is troublesome because the characters present in a words are somewhat similar to other character or connected words may have problem in recognition as number of modifiers are present. The major challenge faced was removal of header line as header line cannot be always straight as it varies from person to person. The characters which are handwritten will not always have sharp corners, the header lines present will not be perfectly straight and the curves which are present will not be so smooth. Handwritten character recognition undergo three major steps (i) Pre-Processing (ii) Feature Extraction(iii)Classification. Pre processing is the first step which deals with binarization, noise removal, morphological operations and segmentation. Segmentation is major part in character recognition. Words are segmented into single single characters and these segmented characters are used for feature extraction. In second step Histogram of Oriented Gradients (HOG) is used as extraction of feature in an image so as to obtain the feature vector .Object detection can be easily done by using HOG in image processing and computer vision. HOG has intensity values which is obtained by gradient computation and will give rough idea of shape or pattern of an image. Last step concludes with classification, for classifying the samples Support Vector machine is implemented. SVM is basically used as binary classifier but in this project it has been used as Multiclass Classifier (One Vs. All). SVM constructs a hyper plane as data points are mapped into higher D-dimensional space. Non Linear SVM includes various kernels like polynomial kernel, radial basis kernel for mapping the data into higher D-dimensional space. The performance analysis is efficient for the kernels which are used. Accuracy rate can be improved for segmentation by using various other methods for segmentation. It can be extended to work on degraded text or broken characters and conversion of text to speech. Online recognition of character can be done.