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41#
發(fā)表于 2025-3-28 16:45:06 | 只看該作者
Software Process Definition and Modelling,medium to low quality; hence they require a sophisticated recognition algorithm capable of properly extracting the correct text from low quality cursive documents. The Dynamic Time Warp (DTW) algorithm is among the most effective algorithms for cursive writing optical character recognition (OCR). Ho
42#
發(fā)表于 2025-3-28 19:46:17 | 只看該作者
https://doi.org/10.1007/978-3-540-33253-4certainly present their own challenges to this process, and here we describe our data creation and annotation efforts for Arabic document analysis. The resulting corpora include both on-line and off-line handwritten data as well as logos, signatures, and mixed-script machine-printed text. All these
43#
發(fā)表于 2025-3-29 00:30:06 | 只看該作者
An Assessment of Arabic Handwriting Recognition Technologycripts. An assessment of the technology for Arabic handwriting recognition is provided based on the published literature. An introduction to the Arabic script is given followed by a description of algorithms for the processes involved: segmentation, feature extraction, classification, and search. Ex
44#
發(fā)表于 2025-3-29 05:33:09 | 只看該作者
Layout Analysis of Arabic Script Documentsnt into a searchable electronic representation. Projection methods are typically employed for extraction of text lines in Arabic script documents. Although projection methods achieve good accuracy on clean, skew-free documents, their performance drops under challenging situations (border noise, skew
45#
發(fā)表于 2025-3-29 08:38:02 | 只看該作者
46#
發(fā)表于 2025-3-29 14:56:41 | 只看該作者
47#
發(fā)表于 2025-3-29 15:49:30 | 只看該作者
Segmentation of Ancient Arabic Documentsone, the segmentation will be limited to textual areas or to line extraction in the areas. Although this type of segmentation appears quite simple, its implementation remains a challenging task. This is due to the state of many old documents; the image is of low quality, and the lines are not straig
48#
發(fā)表于 2025-3-29 20:31:59 | 只看該作者
Features for HMM-Based Arabic Handwritten Word Recognition Systemses. In this chapter we explore various types of features which are popular for Arabic cursive handwriting recognition. Some of these features are statistical, based on pixel distributions or local directions. Others are structural, based on the presence of loops, ascenders, or descenders. We show ho
49#
發(fā)表于 2025-3-30 03:16:57 | 只看該作者
Printed Arabic Text Recognitionripts, overlapping characters, large number of dots and diacritics, etc. In this chapter, we present a general framework for a printed Arabic text recognition system. We then discuss different phases of such a system, e.g., pre-processing, feature extraction, and classification. We present different
50#
發(fā)表于 2025-3-30 06:01:05 | 只看該作者
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