The letter and/or sound combinations that make up a human language are limited by the human's ability to pronounce tnese sounds° Therefore, the standard library search, which as a rule looks for all possible combinations of letters to find a word, is wasteful. Certain letters simply cannot be followed by certain other letters and a search for them is senseless. Following this same line of reasoning, letters very frequently occur in the combinations that are germane to the particular language. The growing amount of alphanumeric information presently being stored on magnetic tape presents increasingly difficult problems in both the number of tape reels used and the time necessary to search this mass of information in order to extract pertinent literature. At the present time most of this literature on tape utilizes the standard IBM 6-bit code to express alphanumeric symbols. ~t is entirely feasible to record standard English literature on tape -be it professional abstracts or novels -using only approximately two-thirds of the binary bits utilized to represent the same piece of written material in the conventional code. This can be accomplished by setting up, in a 9-bit code, the 400-odd letter combinations occurring most frequently. A 9-bit representation allows the programmer to set up as many as 512 symbols, thus leaving sufficient leeway to assign symbols to the most frequentlyused words, mathematical symbols, professional expressions, that are expected to be encountered in the literature to be recorded. In addition, these relatively short 9-bit symbols can be assigned to all key words that it may be necessary to look for later, thereby accelerating any future library search.

Information storage and retrieval

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Offline Recognition of Devanagari Script: A Survey

A survey of document image word spotting techniques

In this paper, we propose a novel technique for word spotting in historical printed documents combining synthetic data and user feedback. Our aim is to search for keywords typed by the user in a large collection of digitized printed historical documents. The proposed method consists of the following stages: (1) creation of synthetic image words; (2) word segmentation using dynamic parameters; (3) efficient feature extraction for each word image and (4) a retrieval procedure that is optimized by user feedback. Experimental results prove the efficiency of the proposed approach.

/pdf/keyword-guided-word-spotting-in-historical-printed-documents-2m0nk7e8v0.pdf

Keyword-guided word spotting in historical printed documents using synthetic data and user feedback

This study surveys current trends/methods in video retrieval. The major themes covered by the study include shot segmentation, key frame extraction, feature extraction, clustering, indexing and video retrieval-by similarity, probabilistic, transformational, refinement and relevance feedback. This work has done in an aim to assist the upcoming researchers in the field of video retrieval, to know about the techniques and methods available for video retrieval.

/pdf/a-survey-of-content-based-video-retrieval-nr6co08mdu.pdf

A Survey of Content-Based Video Retrieval

As large quantity of document images is getting archived by the digital libraries, there is a need for an efficient search strategies to make them available as per users information need. In this paper, we propose an effective word image matching scheme that achieves high performance in the presence of script variability, printing variation, degradation and word-form variants. A novel partial matching algorithm is designed for morphological matching of word form variants in a language. We formulate feature extraction scheme that extracts local features by scanning vertical strips of the word image and combining them automatically based on their discriminatory potential. We present detailed performance analysis of the proposed approach on English, Amharic and Hindi documents.

Matching word images for content-based retrieval from printed document images

This paper presents a system for retrieval of relevant documents from large document image collections. We achieve effective search and retrieval from a large collection of printed document images by matching image features at word-level. For representations of the words, profile-based and shape-based features are employed. A novel DTW-based partial matching scheme is employed to take care of morphologically variant words. This is useful for grouping together similar words during the indexing process.The system supports cross-lingual search using OM-Trans transliteration and a dictionary-based approach. System-level issues for retrieval (eg. scalability, effective delivery etc.) are addressed in this paper.

/pdf/retrieval-from-document-image-collections-3rp1xr7wlf.pdf

Retrieval from document image collections

https://faculty.iiit.ac.in/~anoop/papers/SearchBySynthesis.pdf

Retrieval of online handwriting by synthesis and matching

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available information technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that report the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we propose a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer.

/pdf/optical-character-recognition-of-amharic-documents-2jdklv51h1.pdf

Optical Character Recognition of Amharic Documents

Searching in scanned documents is an important problem in Digital Libraries. If OCRs are not available, the scanned images are inaccessible. In this paper, we demonstrate a searching procedure without an intermediate textual representation. We achieve effective retrieval from document databases by matching at word-level using image features. Word profiles, structural features and transform domain representations are employed for characterising the word images. A novel partial matching approach based on dynamic time warping (DTW) is proposed to take care of word form variations. With the new partial matching procedure, morphologically variant words become similar in image space. This is specially useful for grouping together similar words for indexing purpose. We extend our formulation for cross-lingual search with the help of transliteration.

/pdf/searching-in-document-images-4fqmtwjnop.pdf

Million Meshesha

Papers

Matching word images for content-based retrieval from printed document images

Retrieval from document image collections

Retrieval of online handwriting by synthesis and matching

Optical Character Recognition of Amharic Documents

Searching in Document Images.