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51#
發(fā)表于 2025-3-30 08:56:44 | 只看該作者
RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scriptsof an HMM-based recognition system to handle multiple fonts, different handwriting styles, and their variations. Most current HMM approaches are HTK-based systems which are maximum likelihood (ML) trained and which try to adapt their models to different writing styles using writer adaptive training,
52#
發(fā)表于 2025-3-30 16:27:10 | 只看該作者
53#
發(fā)表于 2025-3-30 16:49:11 | 只看該作者
54#
發(fā)表于 2025-3-30 22:50:13 | 只看該作者
Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networksly, with sophisticated pre-processing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. This chapter considers an alternative system, based on multidimensional recurrent neural networks, that learns directly from pixel data, and desc
55#
發(fā)表于 2025-3-31 00:57:14 | 只看該作者
56#
發(fā)表于 2025-3-31 06:58:34 | 只看該作者
57#
發(fā)表于 2025-3-31 11:25:58 | 只看該作者
58#
發(fā)表于 2025-3-31 16:20:42 | 只看該作者
Data Collection and Annotation for Arabic Document Analysiscertainly 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
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