Cursive Text Recognition in Natural Scene Images Using Deep Convolutional Recurrent Neural Network

Abstract

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from https://data.mendeley.com/datasets/k5fz57zd9z/1.

A Multi-Layer Holistic Approach for Cursive Text Recognition

“…Considering text as object, the existing methodologies can be applied for text detection. This concept is mainly followed in the detection and recognition of text in different languages including English [46][47][48][49], Chinese [50][51][52], Arabic [53,54], Persian [55,56], Urdu [34,[57][58][59][60] and Hindi [61][62][63]. In some languages (including Urdu), there exists multiple ligatures and orthography.…”

CRNN model for text detection and classification from natural scenes

“…Images of natural scenes often include text that can be used for various purposes such as automatic license plate identification, image retrieval, satellite navigation, guiding robots on their way, street sign recognition, and a better understanding of the images themselves [1], [2]. Although natural scene text recognition has come a long way, it is still a complex process because of factors including complicated backdrops, varying text size, color, orientation, low resolution, occlusion, environmental noise, and blur [3].…”

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

“…U-net [ 22 ] is based on FCN [ 23 ] and exhibits the advantages of good performance, low data requirement, and high speed. Various segmentation methods based on deep convolutional neural networks (CNNs), including crowd counting [ 24 , 25 ], text recognition [ 26 , 27 ], and medical image analysis [ 3 , 28 , 29 , 30 , 31 ], have been proposed and are widely used to obtain statistics on target objects [ 32 , 33 , 34 ].…”