Method for Line Segmentation in Handwritten Documents with Touching and Broken Parts in Devanagari Script

days, a vast research is going in Optical Character Recognition (OCR) of handwritten Documents in Indian scripts. A lot of handwritten data is existed in Devanagari script which is still to be recognized. Segmentation is the key step of OCR process. Segmentation is the process of extracting the valuable segments from the text document which are used in the process of recognition of characters. Line segmentation is the process of segmenting the text document into lines. Afterwards, word segmentation and character segmentation is carried out. This paper only deals with the Line segmentation of handwritten documents in Hindi. Devanagari script is the basic script to write Hindi, Marathi, Sanskrit and Nepali languages. In this paper the brief introduction of various existing techniques for segmentation of handwritten text is discussed. Also, develops an algorithm for segmentation of skewed lines, touching lines present in the text document and broken parts in upper modifiers or space present between the upper modifiers. This algorithm is implemented on large database collected from various writers. The proposed algorithm integrated the Projection based method, gap detection between text lines and neighbor pixel analysis method.

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Comparative Study of Text Summarization Methods

Text summarization is one of application of natural language processing and is becoming more popular for information condensation. Text summarization is a process of reducing the size of original document and producing a summary by retaining important information of original document. This paper gives comparative study of various text summarization methods based on different types of application. The paper discusses in detail two main categories of text summarization methods these are extractive and abstractive summarization methods. The paper also presents taxonomy of summarization systems and statistical and linguistic approaches for summarization.

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Segmentation of Connected Components and Overlapping Lines in Gurumukhi Handwritten Documents

Text line segmentation is extremely important phase of OCR. Overlapped lines, skewed lines and connected components make the problem of line segmentation more complicated in Gurumukhi handwritten documents. The existence of these problems in handwritten text documents declines the performance of OCR system. In this paper, we present a technique to solve these problems. The proposed algorithm is based on mid-point detection. The algorithm deals with these problems and gives effective results 90% in case of overlapped lines and 94% accurate results for segmentation of connected components between neighboring lines. This paper also provides a review on major problems in line segmentation that decreases the accuracy of recognition system. The proposed method has achieved 93.05% accuracy in text line segmentation.

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