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11#
發(fā)表于 2025-3-23 13:05:49 | 只看該作者
12#
發(fā)表于 2025-3-23 14:47:36 | 只看該作者
13#
發(fā)表于 2025-3-23 19:00:18 | 只看該作者
Building Data Sets for Indian Language OCR Researchges. In this chapter, we present our activities in this direction. Our corpus consists of more than 600000 document images in Indian scripts. A parallel text is aligned to the images to obtain word- and symbol-level annotated data sets. We describe the process we follow and the status of the activities.
14#
發(fā)表于 2025-3-23 23:15:08 | 只看該作者
15#
發(fā)表于 2025-3-24 05:41:56 | 只看該作者
16#
發(fā)表于 2025-3-24 07:47:56 | 只看該作者
https://doi.org/10.1007/978-3-322-94710-9te on a fair test set consisting of scanned images (scans of printed versions of the synthetic images) was 1.40% while the character error rate on a fair test set of fax images (scans of printed and faxed versions of the synthetic images) was 8.7%.
17#
發(fā)表于 2025-3-24 14:19:20 | 只看該作者
18#
發(fā)表于 2025-3-24 15:58:09 | 只看該作者
Scenario-Based Reading Techniques,iple scripts. This requires intelligent solutions which scale across different scripts. We present a script-independent keyword spotting approach for this purpose. Experimental results illustrate the efficacy of our methods.
19#
發(fā)表于 2025-3-24 21:39:20 | 只看該作者
A Complete OCR System for Tamil Magazine Documentsd from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.
20#
發(fā)表于 2025-3-25 02:30:57 | 只看該作者
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