Optical Character Recognition (OCR) is an important technology. The Arabic language lacks both the variety of OCR systems and the depth of research relative to Roman scripts. A machine learning, HaarCascade classifier (HCC) approach was introduced by Viola and Jones (Viola and Jones 2001) to achieve rapid object detection based on a boosted cascade Haar-like features. Here, that approach is mod...

This show and tell paper describes a client mobile application for Chinese-Spanish machine translation. The system combines a standard server-based statistical machine translation (SMT) system, which requires online operation, with different input modalities including text, optical character recognition (OCR) and automatic speech recognition (ASR). It also includes an index-based search engine ...

Document analysis often starts with robust signatures, for instance for document lookup from low-quality photographs, or similarity analysis between scanned books. Signatures based on OCR typically work well, but require good quality OCR, which is not always available and can be very costly. In this paper we describe a novel scheme for extracting discrete signatures from document images. It ope...

In this paper, we present a new approach to extracting the target text line from a document image captured by a pen scanner. Given the binary image, a set of possible text lines are first formed by nearest-neighbor grouping of connected components (CC). They are then refined by text line merging and adding the missed CCs. The possible target text line is identified by using a geometric feature ...

This report presents the findings of an investigation to evaluate the conditions for search retrieval successes and failures when using uncorrected OCR for indexing. The purpose of the study was to assess whether low-cost, high-production techniques for text conversion were adequate to produce digital reproductions of consistent quality and usability. We sought to identify attributes of the ori...

Optical character recognition (OCR) of natural languages, both typeset and handwritten, is successfully used today in a wide range of applications. OCR of mathematical expressions and mathematical symbols is not yet as advanced, however. This project demonstrates a method for recognising typeset mathematical symbols. The method involves using spectral methods to perform semi-supervised clusteri...

Knowledge extraction by just listening to sounds is a distinctive property. Speech signal is more effective means of communication than text because blind and visually impaired persons can also respond to sounds. This paper aims to develop a cost effective, and user friendly optical character recognition (OCR) based speech synthesis system. The OCR based speech synthesis system has been develop...

In this paper, we describe a system for Android smartphones which detects and extracts Chinese text, and translates it into English. Most of the processing is done on a server; the user simply takes a picture with no other input. In this project we are mainly concerned about the text detection part, for which we use the MSER algorithm; the Optical Character Recognition (OCR) is based on Tessera...

Script segmentation is an important primary task for any Optical Character Recognition (OCR) software. Especially, in case of off-line OCR for printed character, it has more importance. Through script segmentation a big image of some written document is fragmented into a number of small pieces which are then used for pattern matching to determine the expected sequence of characters. In the impl...

The Telugu OCR systems available in the market currently recognize only the specific fonts of Telugu. This paper describes the development of a multi-font OCR system for printed Telugu characters using Artificial Neural Networks. In this system classification of the characters is carried out using multi layer neural network Architecture.