Deep OCR for Arabic script‐based language like Pastho

Abstract

Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature-based recognition system for the cursive Pashto script using four pre-trained CNN models using a fine-tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub-word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre-trained models used on the ImageNet Dataset. These models are later fine-tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST-NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.

Arabic Optical Character Recognition: A Review

“…The architecture includes four convolutional layers, two max pooling layers, and a fully connected layer with a dropout of 20% of the nodes. Naz et al [82] proposed Pashto ligature (subword) OCR approach. The approach achieved 99.31% using the DenseNet CNN architecture.…”

Corpus-based technique for improving Arabic OCR system

“…OCR process converts image of printed or handwritten text into digital text that can be modified, processed, searched, and copied [7]. Although there are still several shortcomings of the technology that need to be treated and resolved to raise accuracy, OCR is a mesmerizing technology that has shouldered computers to digitize texts [8]. The manipulation process includes segmentation image into lines, words and parts of words, thus segmenting the word image into images of characters.…”