Ancient indian document analysis using cognitive memory network

Ancient palm leaf manuscripts in Thailand have been preserved by many organizations for the protection and retrieval of traditional knowledge. With advanced computer technology, digitized media is now commonly used to record these documents. One objective of such work is to develop an efficient image processing system that could be used to retrieve knowledge and information automatically from these manuscripts. Binarization is an important stage during preprocessing of the manuscripts for subsequent extraction of text and characters. The output is then used for further processes such as character recognition and knowledge extraction. However, there is no single binarization technique that is suitable for all documents. This study aims to improve the binarization process intelligently by applying machine learning techniques to classify the optimal selection of binarization techniques of palm leaf manuscripts. Experiments results are reported and this technique could be applied for automatic selection of binarization techniques in other image processing problems.

Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN with Doc-UFCN on a private historical database, and a third comparing the handwritten text recognition (HTR) performance of the tested networks. The results showed that Mask-RCNN outperformed ARU-Net, dhSegment, and Doc-UFCN using relevant line segmentation metrics, that performance evaluation should not focus on the raw masks generated by the networks, that a light mask processing is an efficient and simple solution to improve evaluation, and that Mask-RCNN leads to better HTR performance.

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.

Palm leaf manuscripts are important source of history and ancient wisdom. Large number of manuscripts have been already digitized in the form of folio images. To extract useful information, an optical character recognition (OCR) is often considered to be the first step towards text mining. Unfortunately, folio images contain multiple unsegmented palm leaf images, making it difficult to manage in OCR process. This motivates us to propose a new page segmentation method for palm leaf manuscripts. This method consists of two main steps, first of which is the detection of objects in folio images using Connected Component Labeling method in a transformed L*a*b* color space. The second step is rule-based selection of objects as either palm leaf or not palm leaf. The experiments performed on 20 publicly available palm leaf manuscripts composed of 384 folio images demonstrated that the proposed method effectively segmented folio images into separate palm leaf images, with 99.86 % precision and 96.67 % recall scores.

Optical Character Recognition is considered one of the fastest methods of data entry. OCR converts the text image representation of x and y coordinates representing pixel information to be converted into text data in a particular language. OCR as a field of pattern recognition and document image understanding, OCR requires a challenging job once a different language text is available on the image. Difference in language script will pose different challenges for OCR which requires entirely different approaches and algorithms. Latin scripts require a different approach whereas the Arabic adopted language scripts require a different approach. In this regard, various solutions have been proposed for different languages. Segmentation is considered one of the important tasks in the process of OCR. A good segmentation will definitely increase the accuracy of an OCR. Segmentation includes the segmentation of text lines from text images which are further divided into words. These segmented words are further divided into characters which are to be recognized. A single segmentation algorithm to segment various scripts of the languages is proposed in this study which checks the script and then segments the text image for the further processing in OCR. The proposed generalized algorithm will check the style, direction and other properties of the script and then adopts the segmentation process to segment text lines, words and characters of the language. The proposed algorithm segments more than ten languages of three scripts and segments for their OCRs. These images can be further processed for feature extraction and classification further. The process of OCR for selected languages will be made easier to recognize. Multiple scripts, languages and images were experimented, and the proposed algorithm successfully segmented 32,833 images of text line, words and character image. The algorithm provides 97% accuracy while segmenting these images and can be extended to further languages as well as scripts.

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.KeywordsSegmentationOCRPattern recognitionLine segmentationWord segmentationCharacter segmentationTouching lines segmentationOverlapping lines segmentation

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

Text line extraction is one of the critical steps in document analysis and optical character recognition (OCR) systems. The purpose of this study is to address the problem of text line extraction of ancient Thai manuscripts written on palm leaves, using an Adaptive Partial Projection (APP) technique by integrating a modified partial projection and smooth histogram with recursion. The proposed approach was compared with a Modified Partial Projection (MPP) looking at vowel analysis and touching components of two consecutive lines. The results from this research suggested that the proposed approach for practical data on palm leaf manuscripts has better performance in solving the line segmentation problem.

The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extraction is an essential step in the historical documents image analysis pipeline, as its results significantly impact the accuracy of subsequent processes, such as handwritten text recognition (HTR). Through a multi-stage procedure, we carefully selected 49 peer-reviewed studies published since 2019. Based on careful analysis of these studies, we summarize the information of the existing datasets, describe and categorize different methods, and summarize evaluation protocols. In addition, we compare the results of various methods on benchmark datasets. Finally, we highlight the gaps and suggest directions for future research. We believe that this comprehensive survey will be of great assistance to researchers working in the field of historical document image analysis, as it offers critical insights into the latest advancements and developments, providing a foundation for future research.

Text line segmentation in handwritten documents is an important step in many high level processing such as handwritten document enhancement and text recognition. In this paper we describe a novel approach of text line segmentation based on tracking. In this sense, we consider each connected component in the image as a moving object in its respective line and find its best match given its history motion, i.e. the closest connected component that lie in its trajectory. Direction of motion gives direction of handwritten text and is the output of our tracking algorithm. We apply our approach to images of ICDAR 2013 handwritten segmentation contest and report an overall detection rate of $$86.51\%$$ .

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

The application of deep neural network technologies to the problem of handwriting recognition in pre-reform Russian is considered. The initial data used are scanned JPG images of historical documents from the 19 th century, in particular containing various noises and interference, which complicates the work of the recognition algorithm. Text recognition is performed in three stages: noise removal, segmentation (highlighting) of text lines in the image, since the input data for the deep neural network are precisely the lines, and then recognition of the text of the highlighted lines using the pre-trained Tesseract OCR model, which performs electronic translation of images of handwritten or printed text into text data. The model used is a convolutional recurrent neural network; the model is a combination of a convolutional neural network for extracting local features from an image and a recurrent neural network represented by two layers of bidirectional LSTM networks for processing the sequence. Using this model allows for reliable recognition of handwritten text.

Text line segmentation in unconstrained handwritten documents remains a challenge because handwritten text lines are multi-skewed and not obviously separated. This paper presents a new approach based on the variational Bayes (VB) framework for text line segmentation. Viewing the document image as a mixture density model, with each text line approximated by a Gaussian component, the VB method can automatically determine the number of components. We extend the VB method such that it can both eliminate and split components and control the orientation of text line lines. Experiments on Chinese handwritten documents demonstrated the effectiveness of the approach.

Due to the presence of high volume of noise in text documents, it becomes very difficult to achieve high accuracy while performing text line segmentation. Hence, approaches which are dependent on the performance of the line segmentation stage, also suffers. Also due to the variability of noise patterns, the text contents get distorted and sometimes it results in broken strokes of a character. Thus, character level recognition becomes a challenging task for noisy documents. Our proposed method is aimed to address the aforesaid challenges. Since, we are considering the input image to be of noisy kind, a convolutional neural network (CNN) architecture is introduced to initially identify whether the input image contains noise or not. We consider this as a two-class problem and try to identify the respective classes, i.e. noisy or clean. Then, we apply the proposed piecewise projection profile feature and an adaptive region growing based two-stage algorithmic approach which initially identifies the text line upper and lower boundaries in a noisy text document image and then try to regroup the broken strokes of a character to enhance the character recognition accuracy. The proposed method has been tested on a large size dataset containing noisy documents and also different quality measures are computed to establish the effectiveness of the proposed method. From the measures, it can be noted that the text line segmentation accuracy is comparable to other similar state-of-the-art results.

Despite recent advances in the field of supervised deep learning for text line segmentation, unsupervised deep learning solutions are beginning to gain popularity. In this paper, we present an unsupervised deep learning method that embeds document image patches to a compact Euclidean space where distances correspond to a coarse text line pattern similarity. Once this space has been produced, text line segmentation can be easily implemented using standard techniques with the embedded feature vectors. To train the model, we extract random pairs of document image patches with the assumption that neighbour patches contain a similar coarse trend of text lines, whereas if one of them is rotated, they contain different coarse trends of text lines. Doing well on this task requires the model to learn to recognize the text lines and their salient parts. The benefit of our approach is zero manual labelling effort. We evaluate the method qualitatively and quantitatively on several variants of text line segmentation datasets to demonstrate its effectivity.