Abstract
Abstract --In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 93.45%.
Highlights
WITH the advancement of information technology, online and offline usage of digital text is increasing day by day
Efforts are made by researchers, which leads to a nearly complete Optical Character Recognition (OCR) system for advanced languages of the world
In cursive script languages, when various characters combine, an intermediate shape is obtained called ligature. These ligatures are missing in non-cursive script languages, which further make OCR system complex
Summary
WITH the advancement of information technology, online and offline usage of digital text is increasing day by day. OCR for most of the languages got a very mature position in the last 15 years [1,2,3]. Very large scale deployments have been made for OCR systems of non-cursive scripts languages. A mature OCR for cursive scripts languages is still a challenging task. Pashtu OCR (POCR) is far away due to certain major problems this language is facing It is a cursive language written from right to left-hand side. In cursive script languages, when various characters combine, an intermediate shape is obtained called ligature. These ligatures are missing in non-cursive script languages, which further make OCR system complex. Introducing a CNNs based model for the recognition of the numerals of Pashtu text
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