In recent years, many information retrieval, character recognition, and feature extraction methodologies in Devanagari and especially in Hindi have been proposed for different domain areas. Due to enormous scanned data availability and to provide an advanced improvement of existing Hindi automated systems beyond optical character recognition, a new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic is introduced. This first pre-processes and then classifies textual imaged documents into predefined categories. With this concept, this article depicts a feasibility study of such systems with the relevance of Hindi, a survey report of statistical measurements of Hindi keywords obtained from different sources, and the inherent challenges found in printed and handwritten documents. The technical reviews are provided and graphically represented to compare many parameters and estimate contents, forms and classifiers used in various existing techniques.

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

The world is fast moving towards digitalization. In the age of super-fast computational capabilities, everything has to be made digitalized so as to make the computer understand and thereby process the given information. Optical character recognition is a method by which the computer is made to learn, understand and interpret the languages used and written by the human beings. It provides us a whole new way by which computer can interact with human beings, in their own languages. Hence OCR has been a topic of interest for researchers all around the globe in the past decade and research paper involving OCR is increasing day by day. It is seen that efficient algorithms have increased the speed and accuracy of character segmentation and recognition. A substantial amount of work has been done on foreign languages such as English , Chinese etc. but very few paper are there for Indian languages baring a few for Hindi and Bengali. Hence our research work was directed towards development of a novel algorithm for Devanagari character segmentation for Hindi. Hindi is one of the eighteen languages recognized by the Indian constituency. It is also one of the oldest languages and is spoken by millions of people in India. Segmentation and Recognition of this particular language is difficult because of the presence of complex connected characters and presence of shirorekha. A novel approach has been proposed for the segmentation of the connected character. General Terms Pattern recognition, image segmentation.

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Segmentation of Devanagari Handwritten Characters

Multilingual Optical Character Recognition (OCR) is difficult to develop as different languages exhibit different writing and structural characteristics and it is very difficult to generalize their segmentation process. Character segmentation plays an important role in developing OCR for handwritten languages. The exactness of character segmentation is the integral factor of OCR. In this paper, we exploit this limitation and propose a approach based on the polygonal approximation of the word, which works on more than one Indian languages. This work depicts the novel approach for script independent character segmentation of handwritten text utilizing basic structural properties of the languages. Digitally straight line segments (DSS) of the word is obtained by applying Polygonal approximation to the word. The segmentation of character is language independent and works considerably with skew words as well. Experiments are carried out with four popular Indian languages, Hindi, Marathi, Punjabi, and Bangla. The average success rate for character segmentation of four languages is 90.07% which is satisfactory compared with other existing methods. We use shadow and cumulative stretch feature set with random forest, support vector machine (SVM), multi-layer perceptron (MLP), and convolutional neural network (CNN) classifiers for character recognition. On experimentation, it is observed that our proposed method provided good accuracy for character segmentation and recognition.

Handwritten multilingual word segmentation using polygonal approximation of digital curves for Indian languages

Handwritten Marathi Compound Character Segmentation Using Minutiae Detection Algorithm

OCR remains a challenging area of research for past few years. Accuracy of OCR primarily depends on how correctly characters are segmented. This makes segmentation a crucial step. This work describes the novel technique for character segmentation of handwritten Hindi text using basic structural properties. The proposed method first uses the polygonal approximation to get the digitally straight line segment of the word. On these segments, we apply traversing algorithm which results into segmentation of characters. The success rate is 95.70%, which is satisfactory comparing with other existing methods.

An Efficient Character Segmentation Approach for Handwritten Hindi Text

Finding Structural Layout, Text Line Segmentation, Word Level Segmentation and Character Level Segmentation is major step in offline OCR systems for Devanagari Script in Document Image Processing. This paper proposes a Word and Character Segmentation method for machine printed Devanagari text. A complete word and character segmentation system for Devanagari printed text is presented here. Sometimes, interline space and fused characters make line segmentation and character segmentation a difficult task respectively. We have tested our method on documents in Marathi scripts. A novel technique of character segmentation for printed Devanagari text is presented here. After removing the Shirorekha (header line) of Devanagari text, the bounding boxes are used to surround the segmented characters. Results obtained from this method are encouraging because of morphological operations. In this method we are proposing some basic morphological operations on the scanned document images and got much better results.

Ambadas Balu Shinde

Papers

Shirorekha extraction in Character Segmentation for printed devanagri text in Document Image Processing