#OCR ARABIC TEXT RECOGNITION SOFTWARE SOFTWARE#

#OCR ARABIC TEXT RECOGNITION SOFTWARE SOFTWARE#

The software can now process Arabic texts with high accuracy, overcoming technical challenges previously associated with recognition of Arabic characters. One of the key technology innovations of Recognition Server 3.5 includes the Arabic OCR functionality developed by ABBYY - a result of several years of research and development. It can also connect with a variety of back-end systems and third-party applications with integration via Scripts, XML tickets, a Web-service API, or a COM-based API. It enables automated, unattended document processing which can be centrally managed and accessed from multiple points within the organization or remotely. Designed to streamline document processing in organizations, the product encompasses all the essential parts of the document capture process: scanning, recognition, document separation, classification, indexing and delivery. The new release features the latest ABBYY innovations in the area of optical character recognition (OCR) technology delivering powerful Arabic OCR and higher accuracy of document analysis, and offers enhanced scanning functionality.ДЄBBYY Recognition Server converts large volumes of paper documents or images into fully searchable electronic text suitable for document archiving, e-discovery, enterprise search and other important business processes. 1.The new release features the latest ABBYY innovations in the area of optical character recognition (OCR) technology delivering powerful Arabic OCR and higher accuracy of document analysis, and offers enhanced scanning functionality.ДЄBBYYВ®, a leading provider of document recognition, data capture, linguistic technologies and services, today announced the release of ABBYY Recognition Server 3.5, an intelligent server-based software for automating document capture across an organization. In particular we are interested in its applicability to Indic languages and scripts. Since the approach makes no language specific assumptions, it should be applicable to a large number of languages. We show from a selection of 33 Telugu books that starting with OCR labels for only 30 % of the collection we can recognize the remaining 70 % of the words in the collection with 70 % accuracy using this approach. This approach is applied to a collection of Telugu books-a language for which no commercial OCR exists. We believe that this is because profile features are more robust to word degradations (common in our documents). It is also shown that profile based features perform much better than SIFT and Pyramid Histogram of Gradient (PHOG) features. It is shown that by using an approximate fast nearest neighbor algorithm based on Hierarchical K-Means (HKM), we can do this accurately and efficiently. In the second stage, a nearest neighbor algorithm compares the remaining word-images to those already recognized and propagates labels from the nearest neighbors. The idea here is that an OCR or a reCAPTCHA like process generates a partial set of recognized words. We describe a Collection OCR which takes advantage of the fact that multiple examples of the same word (often in the same font) may occur in a document or collection. Our method achieves 74.5% accuracy on line by line alignment and 60.5% accuracy when aligning whole pages at time.Д¬onventional optical character recognition (OCR) systems operate on individual characters and words, and do not normally exploit document or collection context. We provide experimental results of our algorithm on a set of 70 pages of historical handwritten material - specifically the writings of George Washington. Third, such algorithms allow us to produce indices in a straightforward manner for handwriting material. Second, such alignment algorithms will allow us to produce large quantities of ground truth data for evaluating handwriting recognition algorithms. First, alignment algorithms allow us to produce displays (for example on the web) that allow a person to easily find their place in the manuscript when reading a transcript. We see at least three uses for such alignment algorithms. Here, we propose an algorithm based on dynamic time warping (DTW) for a word by word alignment of handwritten documents with their (ASCII) transcripts. There are, however, few tools available for manipulating handwritten pages. Today's digital libraries increasingly include not only printed text but also scanned handwritten pages and other multimedia material.

Ocr arabic text recognition software