###### Abstract  
Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets. Author identification; statistical-based features; image-based features; deep learning; CNN +
Footnote †: journal:  
0000-0001-6700-0001-6700][PERSON]  
1 Department of Computational Science, Brainware University, Kolkata 700125, India 1  
[PERSON]  
2 Department of Computer Science, West Bengal State University, Kolkata 700126, India 2  
[PERSON]  
3 Techno Main Sultake, Kolkata 700091, India 3  
identification or profiling started back in the 19 th century when [PERSON] [1] characterized the writing pattern of different authors using the frequency distribution of terms of varying lengths. In the early 20 th century, various statistical features were studied involving metrics for stylometry such as Zipf's distribution and [PERSON]'s K measure. Later, [PERSON] and [PERSON] [2] worked with Bayesian statistical measures where the frequencies of a small set of function words were analyzed as the stylistic features of the text. Many other different measures have been explored to extract stylistic features from the text for author identification tasks. Recently, deep learning has been studied in various natural language processing tasks, providing better performance when compared to previous state-of-the-art approaches. In this paper, we propose a system that uses visual features along with a five-layer convolutional neural network for the identification of authors through the recognition of distinct writing patterns. Statistical as well as internal features were extracted and represented by three different types of charts (line, imagesc, and pie) that were then fed to a convolutional neural network. The experiments were done on 1200 literary articles written by 50 different authors from different generations, obtaining a maximum accuracy of 93.58%. Furthermore, the system performance was tested with a varied number of authors (from 5 to 50) to see how the features work on an incremental number of authors (and dataset sizes), as well as with a different number of features to perceive their discrete aspects on the studied datasets. The work was performed on the Bangla language as it is the sixth most spoken language with approximately 265 million users in the world [3]. Since no such corpus was available in Bangla, we built one consisting of 1200 articles written by 50 different authors from different generations, totaling 323,780,594 tokens. The proposed system uses image-based features along with a lightweight CNN. This helps to visualize the writing patterns of an author (by observing the charts) rather than observing the relevant keywords by reading the articles. The organization of the paper is the following: Section 2 presents a brief literature study on the existing research in this domain; Section 3 discusses the proposed methodology and Section 4 presents the experimental setup and discusses the results obtained.