Signature-based writer identification (SWI) is an automated segmentation-free holistic approach where a person is identified based on their handwritten signature. Earlier research attempts mainly featured learning-based approaches where writing patterns were detected and fed to machine learning models for determining the writer. Nowadays, a deep learning-based approach is becoming very popular and several works are reported in the literature using such models. In this paper, we propose a two-stage convolution neural network (CNN) architecture that has two properties: (i) at first, two state-of-the-art CNN models namely VGG-19 and EfficientNet-B0 were truncated making them lightweight; (ii) Secondly, a stack ensemble network (SEN) was proposed where the truncated architectures were stacked along with a shallow base CNN model. The proposed system experimented on a newly built multi-script offline signature dataset where three popular Indic scripts namely: Bangla, Roman and Devanagari were considered. The proposed SEN outperforms individual CNN architectures in terms of recognition rate. In addition, the system converges considerably fast as the SEN architecture is shallower compared to heavier traditional networks. Overall, we obtained the highest writer identification accuracy of 99.44%, 99.04%, and 98.61% for Bangla, Roman, and Devanagari, respectively, by the proposed SEN architecture. Furthermore, the dataset used in this paper will be available freely for research purposes from the link mentioned in Section III.

Shirorekha identification and removal is an important and a challenging pre-processing stage in almost all machine interpretations for handwritten Devanagari documents. Within this area of investigation, all studies are designed based on traditional image processing techniques. Which are mainly based on hand-engineering and learn local transformations only. However, it can also be viewed as a supervised classification task in which each pixel, in a document, is examined/ queried so that those classified as shirorekha are removed. For this purpose, we extended this area of investigation by designing an encoder-decoder based convolutional neural network (EDCNN). Which have demonstrated, from various studies, that they learn image intricacies very well. The contribution of this work is three-fold, first, we created our own handwritten word dataset comprising of words with and without shirorekha, such that, effective training takes place. Next, we trained the proposed network with binary as well as in gray scale formats. Finally, we demonstrated that the proposed approach is accurate and generalizable.

Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation. Language must be identified before we process speech recognition as such tools are language-dependent. We present a language identification system (or AI tool) to distinguish top-seven world languages namely Chinese, Spanish, English, Hindi, Arabic, Bangla and Portuguese [G. F. Simons and C. D. Fennig (eds.), Ethnologue: Laguage of the Americas and the Pacific, Twentieth Edn. (SIL Internatinal, 2017)]. The system uses linear predictive coefficients-based feature, i.e. the line spectral pair–grade ratio (LSP–GR) feature, and ensemble learning for classification. Experiments were performed on more than 200[Formula: see text]h of real-world YouTube data and the highest possible accuracy of 96.95% was received. The results can be compared with other machine learning classifiers.

Speech recognition has established a strong bond with various technological boons for the day to day life of the rustics across the continents. Such advances have not yet propagated to the grassroot level of India, one of the reasons for it being the multilingual nature of our country. We are habituated in using multiple languages while talking, which makes the task of speech recognition challenging thereby making Language Identification an important task. The technique of automatically identifying language from spoken phrases is termed as Automatic Language Identification. The problem of multilingual speech further elevates for South Indian languages which at times become very difficult to distinguish with negligible prior knowledge. In this paper, an Automatic Language Identification System is proposed to distinguish the 4 Dravidian languages which are also known as South Indian languages due to their pre dominant use in the South Indian subcontinent. Dataset size ranged up to the size of 12224 clips and a highest accuracy of 96.46% was obtained by using a newly proposed Line Spectral Pair-Grade (LSP-G) feature along with FURIA based classification technique.

Offline writer identification is one of the major fields of study in behavioral biometric. It is a process of matching a questioned document with other documents of known writers to find the appropriate writer. In this paper, local handwriting-based attributes are used as features, and multi-layer perceptron and simple logistic classifiers are used for decision making. The method is tested on an unconstrained handwritten Bangla database of 1383 documents with variable number of datasets from 190 writers. Experimental results show the effectiveness of our system, since it outperforms the state-of-the-art methods by approximately 3% (top-3 and top-4 choices). Further, our method is approximately 27 times faster than conventional segmentation-based methods.

Script identification is crucial for automating optical character recognition (OCR) in multi-script documents since OCRs are script-dependent. In this paper, we present a comprehensive survey of the techniques developed for handwritten Indic script identification. Different pre-processing and feature extraction techniques, including classifiers used for script identification, are categorized and their merits and demerits are discussed. We also provide information about some handwritten Indic script datasets. Finally, we highlight the extensions and/or future scope of works together with challenges.

We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scripts: Roman and Urdu are considered as negative samples. With this precursor, an overall script identification performance can be advanced by more than 5.13% in accuracy and 1.17 times faster in processing time as compared to conventional system.