Urdu text in natural scene images: a new dataset and preliminary text detection

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PeerJ Computer Science

Introduction

Recent developments within the computer vision and machine learning research communities have witnessed advancement in the task of text detection within natural scene images. Detection of text in natural scene images has applications ranging from robot navigation to understanding of sign prompts by self-driving cars (Yuan, Xiong & Wang, 2016). Text detection in natural scene images is a challenging task due to background complexity, occlusion, different font styles and orientations, variations in scale, and variations in illumination. Detection of text in such complex scenarios leads to false positives (Yao et al., 2012). Most of the work regarding text detection in scene images has been for English and Chinese languages facilitated by the availability of numerous benchmark datasets (Zhu, Yao & Bai, 2016). However, the detection of cursive text (such as Urdu text) from natural scene images has not been investigated yet due to the unavailability of suitable datasets for the training and testing of models.

Some notable developments on text detection can be found in Iqbal et al. (2014), Matas et al. (2004), Shi et al. (2013), He et al. (2016), Iqbal et al. (2017), Yin et al. (2012), Iqbal et al. (2013), and Shahab, Shafait & Dengel (2011). Text detection methods, in general, can be classified into two broad categories: window-based techniques and connected component-based techniques. In window-based techniques, low-level features are extracted by scanning the image several times at different scales. These algorithms, in general, are computationally demanding and time-consuming (Zhang et al., 2013). The connected component based algorithms have been categorized into three classes: region-based methods, texture-based methods, and stroke width transform (SWT) methods (Zhang et al., 2013). In region-based methods, edges and textual properties of text are reliable features that are used in text extraction. Texture-based methods use feature-based approaches to calculate gray level co-occurrence matrix (i.e., calculate features such as homogeneity, dissimilarity, and contrast). In stroke width transform methods, the per-pixel width is considered as a local image operator (Epshtein, Ofek & Wexler, 2010). Stroke is considered as the contiguous part of an image with constant width. Typically, stroke is extracted through image segmentation, extraction of features, and classification. Following this, individual strokes are grouped through clustering. Once a text is detected, text regions are identified by drawing bounding boxes. The coordinates of the bounding boxes can help to locate the text region for extraction. Several approaches for text detection and extraction are discussed in Zhang et al. (2013). Segmentation is used for extraction of the region of interest despite the fact of having background complexity in natural scene images. Thresholding, color clustering, and statistical-based methods have typically been popular, as discussed in Zhang et al. (2013).

Urdu language

There has been a substantial amount of research work reported on text processing of major languages, but no significant work has done been so far for Urdu text detection. Urdu is the national language of Pakistan. It is spoken in more than 20 nations of the world. The number of speakers of Urdu is in the range of 60–70 million. The writing style of Urdu is from right to left. Arabic and Farsi languages have some similarities in writing styles, however, Urdu poses more challenges as it possesses more characters than Arabic and some of these characters have complicated writing styles due to additional diacritics or differences in shapes (Khan et al., 2012). The distinctive characteristics of the Urdu language are; (a) There are no uppercase or lowercase letters. (b) Unlike English, Urdu is written from right to left. (c) Unlike the English text, the words of Urdu are not squared in shape

One key reason behind the rare consideration of Urdu text retrieval is the unavailability of the Urdu text images dataset. Therefore, one of our contributions is a benchmark dataset for researchers to kick start research development on Urdu text extraction and processing. Besides, our proposed framework can be a baseline to compare Urdu text detection in natural scene images. Hence, we propose a framework for Urdu text detection and extraction in natural scene images contained in a dataset developed locally from real scene images. We use channel enhanced maximally stable extremal region (MSER) for text region detection. A two-stage filtering mechanism is applied to eliminate candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, an SVM classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. For classification, an SVM model is trained with the features obtained from the text regions.

The main contributions of this work are:

  • This work presents a new dataset of natural scene images having Urdu text in different fonts and styles.

  • The dataset comprises 500 natural images that are available freely for research use.

  • This work highlights the challenges for Urdu text detection in natural scene images and identifies the knowledge gap due to which research progress on the topic has been slow.

  • This work presents a baseline framework on MSER features for the extraction of Urdu text in natural scene images.

Related Work

Text detection and recognition have greatly been developed for English-based scanned documents, books, and natural scene images (Plamondon & Srihari, 2000). Compared to many other languages, English text detection is relatively less challenging due to the simple style of English letters combination. In Jain et al. (2014), English text is extracted together through the maximally stable extremal region (MSER) features and the use of the Markov Model approach along with support vector machines (SVM). Other approaches used for extraction of English text within scene images include multi-scale thresholding, SWT approaches, conditional random field model approaches, and hybrid approaches with improved recognition accuracy, as discussed in Jain et al. (2014). Besides English, considerable work has been carried out on Chinese text extraction. For example, Chinese text extraction has been performed using MSER-based techniques along with clustering approaches (Jain et al., 2014). While there is no significant work on Urdu text extraction from natural scene images, it is still useful to briefly summarize the work done for many similar languages such as Arabic, Persian and Uyghur languages. Ahmed et al. (2017) have reported a text recognition approach using convolutional neural networks for Arabic text in scene images. The work mainly addressed the recognition of isolated characters of Arabic text within scene images after a total of 2,700 characters have been extracted by segmentation for 27 unique characters (i.e., 100 images per character). Yan et al. (2017) proposed a dataset named IMAGE570 containing Uyghur text scene images. The dataset IMAGE570 contained captioned text images along with scene text images in which 370 images are kept for training data and 200 images for testing data. For text and non-text regions proposal, they proposed channel enhanced MSER technique and for classification, they trained SVM with HOG features. In Darab & Rahmati (2012), a hybrid approach is applied to Persian text detection in scene images. A large dataset of Persian text is presented and state-of-the-art results have been reported. Edge-based features and color information of regions in images are computed for regions proposal. HOG features along with wavelet histograms are computed for text region classification. A local features descriptor technique is applied to detect and recognize text of Korean and Japanese languages. The approach has used scale-invariant feature transform (SIFT) features for extracting text (Zheng et al., 2010). Jamil et al. (2011) proposed edge-based features for Urdu text localization in captioned text video images. The proposed approach for Urdu text detection in captioned text videos is based on edge-based segmentation and few heuristic rules. The detected text regions are then segmented from the background. A dataset of Urdu captioned text video images is proposed by Raza et al., containing 1,000 images of videos (Raza & Siddiqi, 2012). There has been an attempt reported for Urdu handwritten characters recognition by Ali et al. (2020). However, this work addresses individual characters recognition on clean background and text detection in natural scene images is not addressed. Recently, dataset for Urdu text in scene images has been proposed for character extractions and recognition (Chandio, Pickering & Shafi, 2018). The dataset has 600 Urdu text scene images. Text region is manually cropped from these images and made into a dataset of 18,000 Urdu characters of 48 × 48 dimensions for training and testing purpose. Different classifiers are trained with HOG features and efficiency of these classifiers on the proposed dataset is evaluated. Another relevant work on Urdu text recognition is reported by Arafat & Iqbal (2020). The authors have used a deep residual network (ResNet18) for recognition of Urdu text. However, their main contribution is for recognition of text in outdoor images with synthetic text. The synthetic text is a computer-generated text with uniform fonts and size. The results for text recognition in natural scene images are limited to 76.6% accuracy rates on a test set of 110 images.

From the literature review of text detection in natural scene images, we found that little work has been done so far for Urdu text detection in natural scene images. We also found that there is no public dataset available for Urdu text detection in natural scene images. To address these issues and fill the research gap, in this work, we propose a new dataset for Urdu text detection, which is available to the research community for further research purposes. We also contribute a framework for Urdu text detection in scene images with state-of-the-art results.

Dataset

We captured 500 Urdu text scene images in 1080 × 800 resolution. The images are acquired through a smartphone camera with no camera built-in pre-processing filters. The images are acquired in different times of the day to cover diverse lightning conditions. This helps in developing a dataset with images recorded under realistic conditions. Some of the images of the dataset are shown in Fig. 1. Out of these, 400 images are used for training and the remaining 100 images are used for testing purpose. From the training dataset, we manually cropped 7,500 patches of text-regions of size 42 × 46 and 14,500 non-text regions for training the classifier. Samples of the cropped text and non-text images are shown in Fig. 2.

Examples of images from the dataset.

Figure 1: Examples of images from the dataset.

(A) Urdu text regions and (B) Urdu non-text regions.

Figure 2: (A) Urdu text regions and (B) Urdu non-text regions.

Methodology

The overall workflow for the proposed model has four main working modules, as shown in Fig. 3. These four modules are; text region extraction, text region filtering, text character linking, text lines analysis. We discuss each module below.

The workflow of the proposed model.

Figure 3: The workflow of the proposed model.

The four modules are: text region extraction, text region filtering, text character linking, and text lines analysis.

Text region extraction

The connected component-based approaches are popular for their low computational cost. On the other hand, frequency-based approaches for text region extraction are time demanding and computationally expensive. We use the channel enhanced MSER method for Urdu text regions extraction. The channel enhanced MSER technique is popular for its robustness and efficiency. It is adopted for text regions localization. MSER is a connected part of an image whose pixels have a consistent higher or lower intensity than its outer boundary pixels. The MSER extracted region captures the features such as stroke width, aspect ratio, eccentricity, solidity, extent, and Euler number. MSER typically captures the image regions where the local contrast is high.

The MSER works well on grayscale images. However, for blurry images, the performance is poor due to a lack of contrast between the background and the foreground. This is common when RGB images are converted to grayscale images. But in general, most of the foreground has high contrast while the background contents mainly occur in one of the RGB channels than in grayscale. So, Yan et al. (2017) proposed channel enhanced MSER region for text detection. In the channel enhanced method, the MSER regions are detected separately for text in each color channel, and then the detection regions are combined. In this work, for the text regions’ proposal, we apply the channel enhanced MSER method. Figure 4 shows the channel enhanced MSER result on a sample image.

(A–E) Regions detected through channel enhanced MSER.

Figure 4: (A–E) Regions detected through channel enhanced MSER.

In the channel enhanced method, the MSER regions are detected separately for text in each color channel and then the detection regions are combined. The detected regions are shown through red boundary boxes.

Text region filtering

Regions generated by channel enhanced MSER techniques detect true positives along with a large number of false positives. To remove false positive and retain true positives only, a two-step filtering technique is applied.

Geometric filtering

After detecting MSER regions, we apply a few heuristic rules to filter out the non-text regions. Different thresholds are used to discard noise. Thresholds values are not fixed but instead selected manually for a dataset. In geometric filtering, regions are discarded based on geometric properties such as stroke width variation, aspect ratio, eccentricity, solidity, extent, and Euler number (Brooks, 2017). The threshold values of these features are mostly data specific. About 20–25% of non-text regions are discarded at this stage. The heuristic rules for geometric filtering adopted in this work are discussed below.

Overlapping ratio: If two regions share a common region with an overlap ratio greater than 80 percent, then the region with more significant variations is discarded. This helps in filtering out repetitive regions.

Large regions: As we know from MSER, a larger candidate region may have few but not too many smaller candidate regions. So, we define a threshold value to check if the number of small candidates is greater than the specified threshold value. If so, the algorithm discards certain small regions considering them as non-text regions.

Aspect ratio: The ratio between width and height is called the aspect ratio. It is important in discarding non-text regions. It discards regions whose aspect ratio is different from text because the text has a well-defined aspect ratio. In contrast, non-text regions in the background have lower or higher aspect ratios typically.

Eccentricity: The distance between the major axis of the ellipse and the center is called eccentricity. It is helpful for the measurement of the circular properties of the candidate regions. It is also widely used for non-text regions filtering. In text detection, it provides robustness against noise. It is helpful for text region detection as the value provides unique characteristics of the text region.

Euler number: The number of holes subtracted from the number of connected objects is known as the Euler number. In the case of only one region, it is subtracted from one. As the text candidate region can have only a certain range for the Euler number value, it is useful to discard those regions whose number of holes is greater than text candidate regions.

Stroke width variation: In general, the stroke width is smooth for text regions and highly variant for the background. Hence, it is a useful parameter for the elimination of non-text regions. The approach is to set a threshold value of the variance of stroke width. The regions with stroke width variance greater than the threshold value are regarded as noise and hence discarded. It is a widely used criterion for filtering non-text regions in scene images. Still, it cannot be used for Urdu text extraction because the stroke width variation is also high in Urdu text candidates.

SVM filtering

Channel enhanced MSER algorithms detected the text and non-text regions. In geometric filtering, about 20–30% of non-text regions are discarded successfully, but further filtering is required to discard more non-text regions. In this filtering stage, we use an SVM classifier that discards those non-text regions, left after the first stage. SVM is trained with unsupervised features extracted through k-means clustering (Pan, Liu & Hou, 2010). SVM classifier is trained with 23,000 training images of dimension 42 × 46 × 3 having 7,500 text candidates and 14,500 non-text candidates (performance obtained with other dimensions are discussed in the results section). At this stage, most of the non-text regions are discarded but few false-positive candidates are left.

Text characters linking

After geometric and SVM classification, we have mostly all true positive candidates left along with a few false positives. At this stage, we link text characters into pairs and then into lines based on the following rules:

  1. First, we compute the centroids of each region.

  2. The Y difference of two centroids must not be greater than the height of the smaller character for merging them into pair.

  3. Also, the X difference between two centroids must not be greater than two times the height of the larger region.

These conditions ensure that only horizontal and closely located text regions become part of text lines. Regions fulfilling the criteria are combined into text lines, as shown in Fig. 5.

Text regions after SVM classification (top row).

Figure 5: Text regions after SVM classification (top row).

Candidate text regions fulfilling the criteria for text character linking are merged into text lines.

Text lines analysis

After first layer filtering that includes geometric filtering followed by SVM filtering, we got most of the text regions and a few also few non-text candidate regions. Then, in the text character linking stage, we combined text candidate regions to detect horizontal text lines. At this point, we achieve a high recall rate but low precision value. It means that we have detected text lines with more accuracy, but we also have non-text regions detected in the background. So, in the filtering layer of text lines analysis, the detected text lines are verified through different classifiers trained with the histogram of oriented gradients (HOG) features.

Results

The performance of the framework is evaluated on the test set comprising of 100 images containing Urdu text in complex background. The performance evaluation technique is the one proposed in Yan et al. (2017). It is critical to evaluate whether a text region is identified correctly. For the detected text rectangle (DT) and the ground truth rectangle (GT), the overlap ratio proportion is calculated to determine whether a text region is correctly identified. The overlap ratio between DT and GT is defined as:

OverlapRatio=Area(GTDT)Area(GTDT)

If the overlap ratio between ground truth and detected text line is greater than 0.5, then it is considered as true positive; otherwise, the detected text line is a false positive. For single-line text, if we have more non-repeating detected text lines, then DT is the union of all these detected text lines. In the case of multiline text, then GT is the union of all text lines. Once the numbers of true and false positives are computed, we then we measure the performance in terms of precision (p), recall (r), and F-measure (f), as shown in the Eqs. (2)(4) and also used by other works on detection and classification (Do, Le & Le, 2021; Le et al., 2020).

Precision,p=|TP||E|

Recall,r=|TP||T|

Fscore=2pr(p+r)

Here TP represents true positive detected lines, E represents estimated rectangles and T represents ground truth rectangles. Sample images of ground truth are shown in Fig. 6. We have manually cropped 7,500 text characters and 14,000 non-text candidates from 400 images of the training dataset for feature extraction and training classifiers. To compare performances, the detected regions with channel enhanced MSER are resized to three different dimensions i.e., 40 × 36, 46 × 42, and 54 × 50.

Sample images showing ground truth Urdu text regions.

Figure 6: Sample images showing ground truth Urdu text regions.

For evaluation purposes, the manually cropped text and non-text regions from training images are divided into training and test subsets. 5,000 images of both text and non-text regions are selected for training and 1,000 images for each class are kept for testing. For final classification, we train different SVM models. Tables 13 show the experimental results for SVM with linear, Gaussian (radial basis function), and polynomial degree 3 kernels, respectively. The three different resized dimensions are 40 × 36, 46 × 42, 54 × 50. From the comparison, we find that SVM with polynomial kernel on 40 × 36 resized training images achieve the highest accuracy of 0.8880.

Table 1:
Performance of SVM with linear kernel.
Dimension Precision Recall F measure Accuracy
40 × 36 0.8479 0.8454 0.8467 0.8462
46 × 42 0.8424 0.8424 0.8424 0.8417
54 × 50 0.8498 0.8414 0.8456 0.8457
DOI: 10.7717/peerj-cs.717/table-1
Table 2:
Performance of SVM with Gaussian or RBF kernel.
Dimension Precision Recall F measure Accuracy
40 × 36 0.8889 0.8801 0.8845 0.8845
46 × 42 0.8818 0.8722 0.8769 0.8771
54 × 50 0.8909 0.8741 0.8824 0.8830
DOI: 10.7717/peerj-cs.717/table-2
Table 3:
Performance of SVM with polynomial (Degree 3) kernel.
Dimension Precision Recall F measure Accuracy
40 × 36 0.8928 0.8831 0.8879 0.8880
46 × 42 0.8819 0.8880 0.8849 0.8840
54 × 50 0.8892 0.8831 0.8861 0.8860
DOI: 10.7717/peerj-cs.717/table-3

Comparison with logistic regression classifier

For comparison purpose, we also train a logistic regression classifier. Experimental results as reported in Table 4 for this dataset suggest that the performance of the logistic regression was not in par with the SVM classifier.

Table 4:
Performance of logistic regression classifier.
Dimension Precision Recall F measure Accuracy
40 × 36 0.820 0.810 0.812 0.812
46 × 42 0.820 0.814 0.820 0.826
54 × 50 0.820 0.810 0.822 0.810
DOI: 10.7717/peerj-cs.717/table-4

After comparison, we select SVM with polynomial kernel for its better performance. Once the polynomial kernel is selected, then we evaluate the performance of the polynomial kernel from degree 3 to degree 6, as shown in Table 5. As we increase the degree of polynomial beyond degree 5, the performance degrades because of over-fitting. While in the case of a low degree polynomial, the model faces underfitting. The polynomial of degree 5 achieves an accuracy of 0.8980.

Table 5:
Polynomial kernel performance.
Order of polynomial Precision (p) Recall (r) F-measure Accuracy
Degree 3 0.8928 0.8831 0.8879 0.8880
Degree 4 0.9011 0.8850 0.8930 0.8935
Degree 5 0.9077 0.8870 0.8972 0.8980
Degree 6 0.9079 0.8791 0.8933 0.8945
DOI: 10.7717/peerj-cs.717/table-5

The performance of SVM trained with the histogram of oriented (HOG) features with different cell sizes is presented in Table 6. The HoG features are extracted from the training dataset for training SVM in four different blocks. The blocks size are 2 × 2, 4 × 4, 8 × 8, and 12 × 12. We achieve the highest accuracy of 0.8980 with a 4 × 4 block size.

Table 6:
Performance for HoG features with different cell sizes.
Cell size Precision (p) Recall (r) F-measure Accuracy
2 × 2 0.8692 0.8692 0.8692 0.8686
4 × 4 0.9077 0.8870 0.8972 0.8980
8 × 8 0.8975 0.8761 0.8867 0.8875
12 × 12 0.8909 0.8662 0.8784 0.8795
DOI: 10.7717/peerj-cs.717/table-6

After evaluating the performance of different parameters on the proposed dataset, we select those parameters on which highest results are achieved. Those selected parameters are considered for training the classifier from training dataset. Once filter and its different parameters are selected, we then evaluate the proposed methodology on the test set, which contains 100 images of Urdu text in scene images. The text and non-text regions extracted through the channel enhanced MSER regions at text region extraction stage are fed to component analysis where two stages filtering mechanism is used to filter out non-text candidates. In first stage, we filter out non-text regions based on its geometric properties, including aspect ratio, Euler’s number and stroke width variation, etc. At this stage; about 20–25% non-text region are filtered out. In second stage of filtering, SVM is trained with manually cropped text and non-text regions. In Table 7, we compare the results of SVM trained with HOG features and unsupervised features. At component level filtering, we achieve a high f-measure score of 0.3862 of SVM trained with HOG features as shown in Table 7.

Table 7:
Performance comparison of HOG and unsupervised classification on training dataset aftercomponent filtering.
Features Precision (p) Recall (r) F-measure
HOG 0.2609 0.7432 0.3862
Unsupervised 0.2431 0.8021 0.3731
DOI: 10.7717/peerj-cs.717/table-7

At component level filtering, most of the non-text regions along with few text regions are discarded. After component level analysis, we move on to the lines verification stage. In the lines verification stage, non-text lines are pruned by trained SVM with text and non-text lines, which are manually extracted from the training dataset. We manually extracted 700 text lines and 1,400 non-text lines from the training dataset and train SVM with HOG features at that stage. The final results of the detected Urdu text lines on the proposed dataset are shown in Table 7. We achieve a high f-measure score of 0.5469 using unsupervised features at component level filtering followed by lines verification as shown in Table 8. Figure 7 shows some samples of good and bad detection results.

(A) Example images for good text detection on test set images. (B) Example images for bad text detection on test set images.

Figure 7: (A) Example images for good text detection on test set images. (B) Example images for bad text detection on test set images.

Table 8:
Performance comparison of HOG and unsupervised on training dataset after lines verification.
Features Precision (p) Recall (r) F-measure
HOG 0.5057 0.5473 0.5257
Unsupervised 0.4938 0.6128 0.5469
DOI: 10.7717/peerj-cs.717/table-8

Discussion: Urdu text detection and recognition in natural scene images are challenging. Unlike text detection in scanned document images, the task of Urdu text detection in natural scene images has little progress. This work can provide a valuable dataset for further research work and can also serve as a baseline for making progress on this topic. The work has applications in navigation aid devices for individuals with special needs, elderly individuals and developing automatic driving technologies for roads with Urdu navigation signboards.

Conclusion and Future Directions

In this work, we have presented a dataset for Urdu text in natural scene images. The dataset contains 500 images and is freely available for research use. This is the first dataset for Urdu text in natural scene images to the best of our knowledge. Hence, it can provide a very useful baseline for advancing the research on Urdu text extraction and recognition. Secondly, we have presented an approach for detecting Urdu text in natural scene images using MSER features extraction and an SVM classifier. For the text detection task, we have reported performance in terms of F-measure. Several research directions are hereby identified for the research community. The most significant is to develop a text recognition system, which may provide an end-to-end framework for Urdu contents understanding in natural scene images. Besides, the dataset size can be extended by increasing the number of images to several thousand. The dataset presented here mainly contains Urdu text in horizontal orientation. So, a significant contribution would be to add Urdu text with multiple orientations and then develop a text detection method accordingly. Since the dataset itself is now available, the performance of other techniques such as those based on neural networks and deep learning can be evaluated on the dataset. Thus a comparison can be drawn to choose the best possible model.

Supplemental Information

Raw Data: scene images.

DOI: 10.7717/peerj-cs.717/supp-2
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