Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Text Retrieval from Document Images based on N-Gram Algorithm.

Abstract: In this paper, we propose a method of text retrieval from document images using a similarity measure based on an N-Gram algorithm We directly extract image features instead of using optical character recognition Character image objects are extracted from document images based on connected components first and then an unsupervised classifier is used to classify these objects All objects are encoded according to one unified class set and each document image is represented by one stream of object codes Next, we retrieve N-Gram slices from these streams and build document vectors Lastly, we obtain the pair-wise similarity of document images by means of the scalar product of the document vectors Four copora of news articles were used to test the validity of our method During the test, the similarity of document images using this method was compared with the result of ASCII version of those documents based on the N-Gram algorithm for text documents

Robust argumentative zoning for sensemaking in scholarly documents

Abstract: We present an automated approach to classify sentences of scholarly work with respect to their rhetorical function. While previous work that achieves this task of argumentative zoning requires richly annotated input, our approach is robust to noise and can process raw text. Even in cases where the input has noise (as it is obtained from optical character recognition or text extraction from PDF files), our robust classifier is largely accurate. We perform an in-depth study of our system both with clean and noisy inputs. We also give preliminary results from in situ acceptability testing when the classifier is embedded within a digital library reading environment.

Multi-level image thresholding based on Kapur and Tsallis entropy using firefly algorithm

Abstract: Image segmentation is necessity of many application like brain tumor detection, optical character recognition, thermal energy leakage detection, Face recognition etc. multilevel thresholding is the...

Identifying Matching Canonical Documents Consistent with Visual Query Structural Information

Abstract: A server system receives a visual query from a client system, performs optical character recognition (OCR) on the visual query to produce text recognition data representing textual characters, including a plurality of textual characters in a contiguous region of the visual query. The server system also produces structural information associated with the textual characters in the visual query. Textual characters in the plurality of textual characters are scored. The method further includes identifying, in accordance with the scoring, one or more high quality textual strings, each comprising a plurality of high quality textual characters from among the plurality of textual characters in the contiguous region of the visual query. A canonical document that includes the one or more high quality textual strings and that is consistent with the structural information is retrieved. At least a portion of the canonical document is sent to the client system.

An object attribute thresholding algorithm for document image binarization

Abstract: Document image binarization is not a completely solved problem for unconstrained document images. Binarization algorithms, whether global or local, can easily fail on images with noisy or complex background, or poor contrast. The authors report preliminary results on a new approach to document image binarization, an algorithm based on gray scale histogram and run-length histogram analysis. Experimental results on unconstrained machine printed address blocks from the US letter mail stream show that over 99% of such address blocks can be correctly binarized. >