Study of using hybrid deep neural networks in character extraction from images containing text

Character segmentation from epigraphical images helps the optical character recognizer (OCR) in training and recognition of old regional scripts. The scripts or characters present in the images are illegible and may have complex and noisy background texture. In this paper, we present an automated way of segmenting and extracting characters on digitized inscriptions. To achieve this, machine learning models are employed to discern between correctly segmented characters and partially segmented ones. The proposed method first recursively crops the document by sliding a window across the image from top to bottom to extract the content within the window. This results in a number of small images for classification. The segments are classified into character and non-character class based on the features within them. The model was tested on a wide range of input images having irregular, inconsistently spaced, hand written and inscribed characters.

Introduction

Study of ancient inscriptions is important and vital in reconstructing history. The scripts used in these inscriptions may belong to different eras and are classified based on the dynasty that ruled during these periods. Scripts from southern Indian kingdoms form the subject of this study. All South Indian scripts are derived from Brahmi script, which dates back to the 3 century B.C and the script has continuously evolved over the years. These scripts can be seen on temple walls, copper plates, palm leaves, coins and pots. Epigraphers and paleographers are responsible for drawing conclusions from these inscriptions. However, the study of inscriptions takes a lot of time. The automation of this process and construction of an optical character recognizer is the need of the day [1]. Despite the work on inscriptions having improved significantly, period prediction and recognition is still difficult because of the degraded source material and an absence of experts to interpret the obsolete scripts. Traditional ways of decoding these inscriptions comprise creating estampages, manually reading & storing them for further study.

Some of the challenges that are prevalent in the field of historical document restoration are that the text found on inscriptions and estampages does not follow a standard typeset, with fixed character dimensions and regular letter spacing. The same character may be inscribed in different ways by different sculptors. The text is not in a straight line and is found to be degraded through weathering and physical damages inflicted by humans over the course of history. Furthermore, the characters cannot be processed by any character recognition algorithms because the languages being studied are obsolete or have evolved to use a different script. Therefore, models which extract information from inscriptions using techniques that are not dependent on the language or the script are required in these scenarios.

Machine learning models are widely used in solving real world problems. Although they were introduced a long time ago, due to lack of computational power and availability of significant amount of data, usage of these models was limited. Today however, due to the availability of high end machines and Cloud support, layer wise training has taken leaps in every domain. In the recent times, many researchers have found ways to adopt machine learning and its variants in document image analysis, text detection, object recognition and face recognition etc.

Segmentation is the process of dividing an image into its meaningful constituents comprising multiple segments, which can later be used for recognition. In our previous work, nearest neighbor algorithm, water shed algorithm and connected component algorithm were applied and we could conclude that connected component algorithm was better suited for segmentation of epigraphical images. The output also contains unwanted elements, prominently, noise and partially cropped incomplete characters. Segmenting such characters is the main challenge of the models. In this paper, we explore the use of text and non-text classifiers, which are modeled to differentiate noise and half cut characters from legible characters. They are trained to extract and write legible segments onto disk.

The table below summarizes the shortcomings of some of the existing techniques that have been used to segment text Table 1.

Literature survey

Script extraction or character spotting from images of stone inscriptions, palm leaves, degraded historical documents and estampages plays a key role in building automated script recognition. There are several research papers describing segmentation procedures applied on images of printed epigraphical scripts, ancient historical documents and handwritten documents. A survey reveals that input considered in the proposed methods have homogeneous background with equal character spacing and these are the primary reasons for achieving high accuracy. We consider images of Estampages (derived from stone inscriptions) as a type of document. Spotting the characters from these images for the purpose of recognition and prediction remains an unresolved problem. This section will review various sources of literature on character segmentation from epigraphical images along with the challenges encountered.

A.S Kavitha, et al. [5] proposed text segmentation on degraded historical images obtained from Indus Valley civilization remains. Experimentation includes enhancement of input image using a combination of Laplacian and Sobel filters. Closed proximal components of the image are grouped using nearest neighbor method into two clusters - text and non-text. Classifying text and non-text clusters are based on the features of the text. This results in text segmentation of the input image and claims to achieve 90% accuracy.

Printed historical documents that belong to 16th -20th century CE periods are of interest in the paper by Laurence and zahour, et al. [6]. They propose a survey indicating the work done in the field and also explain the importance of epigraphy. A document image undergoes preprocessing to remove noise and is then subjected to segmentation methods like projection profile, bounding boxes and connected components. The methods are applied to images of ancient Arabic documents. The input dataset however dates back to eras as recent as the 16th century, this implies that the data isn’t as degraded as older documents and is less complex to interpret.

A novel segmentation algorithm was designed to work on Greek inscriptions. SLIC super pixel with region merging was adopted to work on geometry of the surface. Based on the uninscribed surface, strokes and breaks on the surface of the rock, surface points are classified [7]. Murthy, et al. [8] proposed a nearest neighbor clustering approach in segmenting the characters. Epigraphical images used in the proposed method are of printed scripts with irregular spaces, skewed, making the lines intersect in certain regions. The authors focus on character, word and line segmentation. In [9] connected component analysis segments the input image into lines and characters. The segmented characters are subjected to recognition and period prediction. Rui Hu, et al. [10], Propose a method to extract Maya codices from degraded images using region based segmentation. Super pixels having multi resolutions are extracted and classified into foreground or background pixels based on SNM classifier. A fully connected conditional random field model is used to improve label consistency. The model helps in retrieval of characters from the Maya script images.

Mohana, et al. [11] propose character extraction from stone inscriptions. The methods applied include cropping the characters, application of morphological operators to enhance script and finally, extracting SIFT features. The cropped reference image is matched with characters of the input image. A reference image is matched with the query image to calculate the correspondence between foreground pixels and matched pixels. The matching factor calculates the ratio of difference in hit and miss count while scanning to total number of foreground pixels. A threshold is set to identify the matching characters in the stone inscriptions. These methods claim to achieve up to 88% accuracy in segmentation of specific characters.

Michele, et al. [12] proposed a method of initializing deep neural network weights with linear discriminant analysis instead of random values. These values are initialized layer wise and converge faster to yield better performance. The transformation matrix obtained from activated LDA features is used to separate text and non-text regions. Experimentation was conducted on historical medieval period dataset of about 150 pages of scanned images. The author worked on small layered CNNs and advices the use of deeper network for further works.

Chen, et al. [13] describe the use of support vector machine to classify, text, borders, background and periphery in their paper. Super pixels derived from an image are used to extract features based on intensity values into classes. Pinkesh, et al. [14] proposed a bidirectional LSTM model to segment a sentence embedding using a CNN model. The text segments are classified from printed documents.

Junho, et al. [15] in their paper, have implemented convolutional neural networks and have used synthesized samples for training. The model segments handwritten text based on these features. The authors’ claim is that the accuracy achieved upon segmentation lies between 75% and 90%. Al-Rawi, et al. [16], describe the use of generative adversarial network to segment text automatically and does not require pixel level annotation of the dataset by constituting reliable unsupervised text segmentation. The data set is mainly composed of scene images containing text. In M M Reza, et al. [17], table segmentation in invoices is the motto for using conditional generative adversarial network for table area localization. Signet based encoder and decoder with skip connections are used for segmentation.

Li, et al. [18], considered character segmentation under complex background and their model classifies the split images into characters and gap images. P-N Learning strategy was adopted to train the CNN, which verified the classification with handwritten text data set. In Zirari, et al. [19], connected component-based approach is used for segmentation. In Manigandan, et al. [20], a binary image was used to segment the characters based on connected components. Sowmya, et al. [21] propose drop fall and water reservoir method to segment the connected characters in historical records of varying complexity. The dataset considered for the model is the set of printed epigraphical characters and works better upon enhancement. In Abtahi, et al. [22], segmentation agent based on reinforcement learning helps in finding the appropriate paths for segmentation along with projection profiles. The input used has one to two text lines and the characters are equally spaced.

Proposed model

Epigraphical images considered for experimentation are estampages from regions covering the modern-day Indian state of Karnataka from various periods from 4th century to 15th century CE. The data has been collected from Archaeological Survey of India, Mysore. The images are inherently noisy due to degradation and the complex texture of the medium on which they are present.

Epigraphical text is harder to segment into letters due to varying and uneven character dimensions. The connected component algorithm uses a graph traversal technique, which works well on image segmentation, but generates image segments having both text and non-text. The term “non-text” refers to noise, cuts and incomplete text. Feeding the output of the connected component technique will yield poor results by models that are designed for recognition and prediction.

To remedy this issue, input images are segmented into multiple small image segments using regular overlapping rectangular kernels of sizes comparable to text size. The dimension of the sliding window is chosen in such a way that it encompasses one character. The generated image segments are grouped into two classes as correctly segmented text and incorrectly segmented text. The process of classification is done automatically by detecting the presence of white border in a binary image. If such a border exists, it is safe to assume that there is no broken text or partially segmented characters, which if present will extend beyond the segment, thereby making it impossible for the segment to have a clean unbroken border.

This data is fed to various machine learning models for classification and correctly classified characters of the text are then written onto the disk. Machine learning models considered for experimentation are Feed Forward Neural Networks, Convolutional Neural Network, K-Nearest Neighbor Model, Support Vector Machine and a combination of CNN and SVM models (Hybrid model). Figure 1 shows the proposed model.

Figure 1: Logic flow of the proposed model of the study. A dataset of segmented characters is built to train the CNN model. The test image is broken down iteratively using sliding windows. Each segment is sent to the model.

a. Generation of dataset for classifier models

The data set of rectangular segments is shown in Figure 2. It consists of correctly segmented and partially segmented characters from epigraphical images. The procedure to generate the data set is as follows. Firstly, A kernel is slid across the image and the contents are stored on the disk periodically. A regular sized rectangular kernel is chosen such that it fits a single character. This kernel is slid across the input image to capture a small region of the image at each iteration. The process continues until kernel reaches end of image. The generated inputs are grouped into two classes - Valid characters and Invalid characters (non-text characters), which constitutes the dataset for all the models.

Figure 2: Small cropped segments which forms the train dataset.

b. Preprocessing

The cropped segments may contain noise and will be of different size since the text size isn’t consistent among the documents. During the preprocessing stage, a median filter is applied to improve quality and the images are resized to 32*48. At a first glance each character is identified by its border and classified into one of text and nontext classes. [23].

c. Neural network

The neural network is a CNN. A feed forward neural network is attached to the convolution layers. This network is trained to classify the input dataset. The network uses RELU activation function for all of its hidden neurons and sparse activation makes the network efficient and easy for computing. Multiple hidden layers are selected based on trial and error and these layers aid in learning linear and nonlinear relationships between inputs and outputs [24].

The architecture adopted for our study (after the initial trial and error classification) includes an input layer which takes a segment of size 32*48. The image is flattened and passed to three hidden layers with activation function as RELU, and propagates its way to the output layer with Sigmoid activation function. The loss is calculated using binary cross entropy and the optimizer used is Adam. To evaluate the model’s efficiency, accuracy and F1 Score are used as metric.

The data set is split into training, testing and validation sets. 10 percent of the data is set aside for validation. 80 percent of the segments is used for training, while the remaining 20 percent of it is used for testing. Epochs are set such that, the network stops once the loss converges to zero.

This method works in all cases where the letters are disjoint. Languages which heavily make use of connected letters cannot be segmented using this method. Output of the model and accuracy are discussed in results section.

d. Convolutional neural network

Convolutional Neural Network was designed to have a series of 2D convolutional layers, each followed by a max pooling layer. Rearranging the layers and changing number of neurons were two things that made it pivotal in reducing overfitting and improving the validation accuracy as opposed to using L1 and L2 regularizes, which improved the training accuracy. Stochastic gradient descent was used to train the model rather than steepest ascent gradient descent [25] for obvious reasons Figure 3.

Figure 3: Convolutional neural networks.

The network has 4 convolutional layers, RELU activation function is applied to each layer. The optimizer used for gradient calculation is Rmsprop and Loss is calculated using Binary Cross Entropy. Given below are the equations that depict the calculation of new weights and bias using Rmsprop optimizer. β value is assigned to 0.9 and ϵ is used to prevent the gradients from blowing up as v_dw can be zero.

$v_{dw}=\beta .v_{dw}+(1-\beta ).d{w}^2\text{ (1)}$

$v_{db}=\beta .v_{dw}+(1-\beta ).d{b}^2\text{ (2)}$

$W=W-\alpha .\frac{dw}{\sqrt{v_{dw}}+\in }\text{ (3)}$

$b=b-\alpha .\frac{db}{\sqrt{v_{db}}+\in }\text{ (4)}$

e. Support vector machine

Histogram of oriented gradients features is constructed and is used to classify the segments into character or non-character from the dataset. Principal component analysis is used for dimensionality reduction and was adopted to normalize features before the SVM algorithm is invoked. SVM, using nonlinear kernel, captures complex relationship between the correctly segmented characters and the incorrect ones. According to the observations made, the time taken is higher and is also computationally intensive [26,27].

HOG feature descriptors are best known for object detection in computer vision problems. The magnitude of gradients along the corners and edges define the object shape. These features are extracted from the input of size 32*48*3. As a first step, vertical and horizontal gradients are calculated using the kernel mentioned below.

Sharp changes in intensity are triggered by magnitude of the gradients. vertical lines are used to depict intensity changes along x axis and horizontal lines along y axis. There are no data points in smooth regions. This typically shows the outline of characters in our image. By analyzing the inherent structure of HOG features and reducing of features using principal component analysis [28], they are subjected to Support vector machines.

Using these data points, SVM generates the best decision line called hyper plane, classifying text and non-text characters. Experimented linear kernel gave considerable outcome with good accuracy.

f. Combination of convolutional neural network and Support vector machine

Individually, the CNN and the SVM models performed poorly. To improve the accuracy further, a model that was composed of CNN and SVM cascaded together was proposed and implemented. On the raw dataset, Convolutional neural network was trained and the results were classified as true positives, true negatives, false positives and false negatives. True positives were written to disk while false positives and negatives were sent to Support Vector Machine model, which constructed a hyperplane to classify text and non-text. The accuracy of the model drastically increased compared to any other model discussed. The results and discussion would showcase more in the next section.

Implementation

The models were tested on Google Colab with 12GB RAM and 80 GPU NVIDIA which accelerated the execution. The code was written in Python 3.5 using Tensorflow, an openended software library used for machine learning algorithm design, as the framework for building neural networks.

To build the dataset, images were fed to a sliding window algorithm, which crops the image into equal-sized segments. It is a brute force technique to generate all possible segments having both text and nontext. The window size is based on the size of the text. The number of generated crops depends on the image size. On the generated segments, a simple border check algorithm is applied and if a border exists it is considered to be text, as text would appear in the middle of the segment. The images are resized to 32*48 in order to create a uniform dataset. Distortion of text is not an issue to the neural network since it is only concerned with whether a character exists within the segment. Nearly 5400 Samples are recorded in the dataset file having details of text and nontext labels with 1 and 0 respectively. Noisy segments may be classified as correct characters if they lie within the segment. In such cases, SSIM is used to compare the output with the set of all acceptable characters of the language to discard unwanted and yet, properly segmented characters. An example of such noise is the presence of characters from a different language Figure 4.

Figure 4: The steps in generation of dataset.

Results and discussion

The performance was evaluated with the help of a confusion matrix. Accuracy is a measure of correctly predicted instances over all the instances made and F1 score is the weighted average of precision and recall. It is a measure of how correctly the model classifies positive instances.

Accuracy is the ratio of correctly predicted observations to the total observations and F1 score is weighted average of Precision and Recall. The equations 5-8 describe the metric as follows:

$\text{Accuracy=}\frac{\text{(TP+TN)}}{\text{(TP+TN+FP+FN)}}\text{ (5)}$

$\text{Precision}\frac{\text{(TP)}}{\text{(TP+FP)}}\text{ (6)}$

$\text{Recall=}\frac{\text{(TP)}}{\text{(TP+FN)}}\text{ (7)}$

$\text{F1 Score = }\frac{\text{2*(Recall*Precision)}}{\text{(Recall+Precision)}}\text{ (8)}$

Table 2 depicts result of all the models and shows that a combination of Convolutional neural network and Support vector machine outperformed in classifying the characters. Upon completion of prediction correctly segmented characters are written on to the disk which can be further used for recognition Figure 5.

Figure 5: The training accuracy of the CNN network and the ROC curve for the SVM model.

From the above table, it can be concluded that the hybrid model outperformed and learnt to classify text and non-text. The models are tested on inputs and outputs are depicted in the below Figures 6-8.

Figure 6: Depicting clear input image and corresponding output segmented text images.

Figure 7: Depicting degraded input image and corresponding output segmented text images.

Figure 8: Depicting a severely degraded historical document and its Output having only one script.

It was found that the Hybrid model outperformed all other models with the Accuracy of 83.758% and F1 Score of 92.06% for the test input images.

Conclusion and future work

From experimentations, Machine learning models can be used to classify segmented images into text and non-text which further can be used for recognition and prediction of time periods. Feed forward neural network, while has a good accuracy, failed to obtain high F1 scores and was remedied by using the hybrid model, which outperformed all the other models. It was both consistent and had fast convergence. With the expense of computational power, the classifiers can converge at greater speeds.

Input images used are preprocessed Estampage images and printed epigraphical scripts. The sliding window applied on these images produced training dataset and the machine learning models were tested on 8000+ segments. Acceptable results were established for epigraphical samples having equal-spaced characters and for white space delimited characters. On the Estampage images (images having noisy, overlapped characters) accuracy of the models decreased because of varying sized characters. The results of segmentation can be further used for prediction of era and its dynasty.

Trained CNN models can be set up with the SVM classifier in a pipeline to automatically perform the process of segmentation with little effort from the end users. The two models when cascaded, create a verification process, where the SVM verifies the results generated by the neural network.

The novelty of the model presented in the article is summarized as follows

1. The use of white space surrounding the black pattern as a separator is independent of alignment of text, size of the line or the dimensions of the character. Skewed text with uneven and unconstrained string of characters are easily segmented. The model performs well even in cases with minor overlap.

2. The model performs well when the document is slightly degraded. It however fails when the noise has the same texture as text.

3. The use of a verification model alongside the primary classification model reduces the need for human verification once the segmented characters are presented. There were very few wrongly classified characters, as indicated by the results table.

The shortcomings of this model are

1. The primary assumption is that characters are segmented based on white boundaries. Any scribble on the document, separated by whitespace is classified as character. The models do not know whether the language encompasses the patterns extracted from the document.

2. The model fails to extract characters from languages such as Hindi, where the characters are required to be joined to form words.

Future work lies in improving the accuracy of segmentation and classification of character/noncharacter by automatically training machine learning models to crop and classify which can build dataset for prediction of era and recognition. These can be implemented by other deep learning models like RCNN, Fast RCNN and Mask RCNN.

We thank Archaeological Survey of India, Mysore for providing the dataset for the project. All the work was done at PES University.

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