Classification is a process to pull out patterns from a number of classes by using various statistical properties and artificial intelligence techniques.
The problem of classification is considered as one of the important problems for the development of applications and for efficient data analysis. Based on the learning adaptability and capability to solve complex computations, classifiers are always the best suited for the pattern recognition problems. This paper presents a comparative study of various classifiers and the results achieved for offline handwritten Gurmukhi characters and numerals recognition. Various classifiers used and evaluated in this study include k-nearest neighbors, linear-support vector machine (SVM), RBF-SVM, Naive Bayes, decision tree, convolution neural network and random forest classifier. For the experimental work, authors used a balanced data set of 13,000 samples that includes 7000 characters and 6000 numerals. To assess the performance of classifiers, authors have used the Waikato Environment for Knowledge Analysis which is an open source tool for machine learning. The performance is assessed by considering various parameters such as accuracy rate, size of the dataset, time taken to train the model, false acceptance rate, false rejection rate and area under receiver operating characteristic Curve. The paper also highlights the comparison of correctness of tests obtained by applying the selected classifiers. Based on the experimental results, it is clear that classifiers considered in this study have complementary rewards and they should be implemented in a hybrid manner to achieve higher accuracy rates. After executing the experimental work, their comparison and analysis, it is concluded that the Random Forest classifier is performing better than other recently used classifiers for character and numeral recognition of offline handwritten Gurmukhi characters and numerals with the recognition accuracy of 87.9% for 13,000 samples.

Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study

In the area of digital biometric systems, the handwritten signature plays a key role in the authentication of a person based on their original samples. In offline signature verification (OSV), several problems exist that are challenging for verification of authentic or forgery signature by the digital system. Correct signature verification improves the security of people, systems, and services. It is applied to uniquely identify an individual based on the motion of pen as up and down, signature speed, and shape of a loop. In this work, the multi-level features fusion and optimal features selection based automatic technique is proposed for OSV. For this purpose, twenty-two Gray Level Co-occurrences Matrix (GLCM) and eight geometric features are calculated from pre-processing signature samples. These features are fused by a new parallel approach which is based on a high-priority index feature (HPFI). A skewness-kurtosis based features selection approach is also proposed name skewness-kurtosis controlled PCA (SKcPCA) and selects the optimal features for final classification into forged and genuine signatures. MCYT, GPDS synthetic, and CEDAR datasets are utilized for validation of the proposed system and show enhancement in terms of Far and FRR as compared to existing methods.

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

As the years passed by, computers became more powerful and automation became the need of generation. Humans tried to automate their work and replace themselves with machines. This effort of transition from manual to automatic gave rise to various research fields, and document character recognition is one such field. From the last few years, there is a sincere contribution from researchers for the development of optical character recognition systems for various scripts and languages. As a result of intensive research and development, there has been a significant improvement in handwritten devnagari text recognition. The main focus of this study is detailed survey of existing techniques for recognition of offline handwritten Hindi characters. It addresses all the aspects of Hindi character recognition starting from database to various phases of character recognition. The most relevant techniques of preprocessing, feature extraction and classification are discussed in various sections of this study. Moreover, this study is a zest of work accepted and published by research community in recent years. This study benefits its readers by discussing limitations of existing techniques and by providing beneficial directions of research in this field.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2017.0184

Handwritten Hindi character recognition: a review

Signature identification and verification are some of the biometric systems used for personal identification. Signatures can be considered as authentication of an individual by the analysis of handwriting style, subjected to inter-personal and intra-personal variations. This paper presents an extensive systematic overview of online and offline signature identification and verification techniques. In offline signature verification, surveys related to two approaches, namely, writer-dependent, and writer-independent approaches are presented. Moreover, the compiled study of feature extraction and classification techniques used for signature identification and verification process has also been incorporated. Several databases introduced in the literature are considered to evaluate different signature identification and verification techniques and corresponding results are reported in this article. The entire survey is further summarized in the form of a table for comparisons. In order to reveal the superiority of the present survey, the comparison of the present survey with the existing recent survey works has also been presented. Finally, future directions are provided for further research.

Signature identification and verification techniques: state-of-the-art work

Large databases of scanned documents (medical records, legal texts, historical documents) require natural language processing for retrieval and structured information extraction. Errors caused by the optical character recognition (OCR) system increase ambiguity of recognized text and decrease performance of natural language processing. The paper proposes OCR post correction system with parametrized string distance metric. The correction system learns specific error patterns from incorrect words and common sequences of correct words. A smoothing technique is proposed to assign non-zero probability to edit operations not present in the training corpus. Spelling correction accuracy is measured on database of OCR legal documents in English language. Language model and learning string metric with smoothing improves Viterbi-based search for the best sequence of corrections and increases performance of the spelling correction system.

https://link.springer.com/content/pdf/10.1007%2Fs11042-016-4185-5.pdf

Learning string distance with smoothing for OCR spelling correction

An Optical character recognition (OCR) consists of the phases: preprocessing and segmentation, feature extraction, classification and post-processing. This paper focuses on preprocessing and segmentation tasks which plays a major role in the subsequent processes of an OCR. The objective of preprocessing and segmentation is to improve the quality of the input image. In addition this phase removes unnecessary portions of the input image that would otherwise complicate the subsequent steps of OCR and reduce the overall recognition rate. Preprocessing and segmentation step consists many sub processes namely, image binarisation, noise removal, skew detection and correction, page segmentation, text or non-text classification, line segmentation, word segmentation and character segmentation. This paper proposes a new method to calculate the skew angle for skew correction. In addition this paper proposes a more accurate method to segment the input image as blocks and classify the blocks as text or non-text. The skew angle is calculated on the scanned document using Wiener filter, smearing technique and Radon transform. Document image is segmented into blocks using run length smearing algorithm and connected component analysis. Features such as basic, density and HOG are extracted from each block for text and non-text classification. The proposed methods are tested on 54 documents. The testing results show a recognition rate of 96.30% for skew detection and correction whereas the recognition rate is 99.18% for text or non-text classification with binary SVMs using RBF kernel.

http://ieeexplore.ieee.org/iel7/7387214/7397173/07397266.pdf

A preprocessing method for printed Tamil documents: Skew correction and textual classification

Optical character recognition (OCR) is one of the important research areas in image processing and pattern recognition. OCR for printed Tamil text is considered as a challenging problem due to the large number of (i.e., 247) characters with complicated structures and, similarity between characters as well as different font styles. This paper proposes a novel approach for multiclass classification to recognise Tamil characters using binary support vector machines (SVMs) organised in a hybrid decision tree. The proposed decision tree is a binary rooted directed acyclic graph (DAG) which is succeeded by unbalanced decision trees (UDT). DAG implements OVO-based SVMs whereas UDT implements OVA-based SVMs. Each node of the hybrid decision tree exploits optimal feature subset in classifying the Tamil characters. The features used by the decision tree are basic, density, histogram of oriented gradients (HOG) and transition. Experiments have been carried out with a dataset of 12400 samples and the recognition rate observed is 98.80%with the hybrid approach of DAG and UDT SVMs using RBF kernel.

A hybrid decision tree for printed Tamil character recognition using SVMs

Skew detection and correction of a scanned document is a very important step in Optical Character Recognition because skew of scanned document is reducing the accuracy of text line approach for skew detection and correction to calculate the skew angle on multi-script scanned document using Radon transform, Hough transform, Harries corner, Wiener filter and smearing algorithm. In this paper, a proposed approach is compared existing skew detection and correction techniques for printed documents having different scripts: English, Tamil, Sinhala and mixed-script. A proposed hybrid method is tested on 160 documents. The overall testing results is 90.62% for skew detection and correction.

/pdf/a-hybrid-approach-for-skew-detection-and-correction-in-the-4i51tl2kvg.pdf

A Hybrid Approach for Skew Detection and Correction in the Multi-script Scanned Document

This research work was carried out in collaboration among all authors. Author WS carried out the research work and wrote the protocol and author MR gave useful advices and managed the literature searches and wrote the first draft of the manuscript. Author ARW supervised, gave useful advices, comments, remarks and engagement through the learning process of the entire research. All authors read and approved the final manuscript.

/pdf/offline-handwritten-signature-recognition-based-on-sift-and-3d7zmet82a.pdf

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Optical Character Recognition (OCR) deals with automated recognition of characters that are in the format of digital image. OCR refers to the process by which scanned images are electronically processed and converted to an editable document. Handwritten and printed texts are the primary research areas of an OCR. Many OCR systems are commercially available for English and Arabic characters but there is still no recognition system available which yields higher recognition rate even though the scanned images are of high quality. The general framework of a Tamil OCR in the literature involves: preprocessing, line segmentation, word segmentation, character segmentation, feature extraction and recognition of characters. OCR for printed Tamil documents poses challenge owing to: one line may have different font styles, presence of pictures, multi columns, touching of adjacent characters, presence of broken characters, low print quality and complex layout. Furthermore, when comparing 26 alphabets in English, Tamil language has 247 alphabets which makes the recognition more difficult. There are few OCRs for Tamil language that are freely available with a moderate recognition rate as the performance comparisons of such OCRs are not available on a benchmark dataset. In this paper we compare OCRs for printed Tamil texts on four different types of documents: books, magazines, newspapers and pamphlets. Furthermore we propose a post-processing error correction technique to the tested OCRs which reduces the overall mean error rate by nearly 10% on those four categories.

M. Ramanan

Papers

A preprocessing method for printed Tamil documents: Skew correction and textual classification

A hybrid decision tree for printed Tamil character recognition using SVMs

A Hybrid Approach for Skew Detection and Correction in the Multi-script Scanned Document

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

A performance comparison and post-processing error correction technique to OCRs for printed Tamil texts