<title>Spotting words in handwritten Arabic documents</title>

Abstract

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 indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20, 000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.

Versatile Search of Scanned Arabic Handwriting

“…Since the number of characters in Arabic is a rather small compared with the number of potential words, a library of component characters can be incorporated directly into the system. This eliminates the indexing phase of [10].…”

“…In [10], the authors noted that a more complex set of features is expected to yield a higher level of performance.…”

“…Unlike the method presented in [10], this method depends on character images rather than word images to form a basis for comparison. In this method, the image is essentially cut into component characters and each character is matched for similarity, in contrast to the latter's method of matching entire word shape.…”

“…A preprocessing step is a line and word segmentation process, such as the one described in [10]. The candidate word image is first split into a sequence of segments (as in Figure 10), with the ideal result being individual characters in the candidate word being separated.…”

“…Automatic word segmentation, as presented in [10], is based on taking several features on either side of a potential segmentation point and using a neural network for deciding whether or not the segmentation is between two distinct words. Some of the differences between the tasks of segmenting Arabic script and segmenting Latin script are the presence of multiple dots above and below the main body in Arabic and the absence of upper case letters at the beginning of sentences in Arabic.…”