In this paper we present a system for searching keywords in Arabic handwritten and historical documents using two algorithms, Dynamic Time Warping (DTW) and Hidden Markov Models (HMM). The HMM based system provides satisfying results when it is possible to provide adequate training samples (which is not always possible in historical documents). The DTW algorithm with a slight modification provi...

The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an inde...

Article history: Received 13 April 2007 Received in revised form 26 March 2008

The automatic recognition of text on scanned images has several applications such as automatic postal mail sorting and searching in large volume of documents. Although Arabic handwritten text recognition has been addressed by many researchers, it remains a challenging task due to several factors. This paper presents an overview of off-line handwritten Arabic character recognition and summarizes...

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. This paper provides a comprehensive review of existing works in handwritten character recognition based on Evolutionary computing technique during the past decade. KeywordsHandwritten Character recognition...

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for Optical Character Recognition on hand written documents. This paper provides a comprehensive review of existing works in handwritten character recognition based on soft computing technique during the past decade. KeywordsHandwritten Cha...

Word segmentation is the most critical pre-processing step for any handwritten document recognition/retrieval system. This paper describes an approach to separate a line of unconstrained (written in a natural manner) handwritten text into words. When the writing style is unconstrained, recognition of individual components may be unreliable so they must be grouped together into word hypotheses, ...

We describe CITlab’s recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multidimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software m...

The digitization of written human knowledge into string data has reached up to but not beyond the recognition of typeset text. This means that vast libraries of handwritten, cursive documents must be indexed and transcribed by a human—a prohibitively laborious task. This paper explores an existing technique developed in [1] and [12] for the offline indexation of historical handwritten documents...

Indexing and searching collections of handwritten archival documents and manuscripts has always been a challenge because handwriting recognizers do not perform well on such noisy documents. Given a collection of documents written by a single author (or a few authors), one can apply a technique called word spotting. The approach is to cluster word images based on their visual appearance, after s...