Newspapers are always a standard medium to convey important information to masses of people in recent time as well as in old time. An automated system is required to convert information into a processable form so that information could be searchable. Many efforts have been done on Gurmukhi script documents in typed or written form, but very few articles are present on Gurmukhi script newspaper text recognition or text and image segmentation. Image/graphics segmentation of text is mandatory before feeding newspaper text to OCR for accurate results. In the literature, many techniques have been proposed for segmenting images and text, but many are complex in nature. In this article, the authors have proposed a very simple and effective hybrid approach based on run length smoothing algorithm and projection profile to segment an image from text in Gurmukhi script newspaper articles. Both horizontal and vertical run length smearing algorithm is used for labeling the regions. Logical AND operator is applied to resultant images to identify the text and image regions. To segment the image region among the labeled regions, projection profile technique is implemented. The combination of these two techniques has produced very good results.

Text and graphics segmentation of newspapers printed in Gurmukhi script: a hybrid approach

Adaptive shock-diffusion model for restoration of degraded document images

Improved integer/fractional order partial differential equation-based thresholding

We describe an intelligent image analysis approach to automatically detect poems in digitally archived historic newspapers. Our application, Image Analysis for Archival Discovery, or Aida, integrates computer vision to capture visual cues based on visual structures of poetic works— instead of the meaning or content—and machine learning to train an artificial neural network to determine whether an image has poetic text. We have tested our application on almost 17,000 image snippets and obtained promising accuracies, precision, and recall. The application is currently being deployed at two institutions for digital library and literary research.

Aida: Intelligent Image Analysis to Automatically Detect Poems in Digital Archives of Historic Newspapers.

Abstract Semantic segmentation has been proposed as a tool to accelerate the processing of natural history collection images. However, developing a flexible and resilient segmentation network requires an approach for adaptation which allows processing different datasets with minimal training and validation. This paper presents a cross-validation approach designed to determine whether a semantic segmentation network possesses the flexibility required for application across different collections and institutions. Consequently, the specific objectives of cross-validating the semantic segmentation network are to (a) evaluate the effectiveness of the network for segmenting image sets derived from collections different from the one in which the network was initially trained on; and (b) test the adaptability of the segmentation network for use in other types of collections. The resilience to data variations from different institutions and the portability of the network across different types of collections are required to confirm its general applicability. The proposed validation method is tested on the Natural History Museum semantic segmentation network, designed to process entomological microscope slides. The proposed semantic segmentation network is evaluated through a series of cross-validation experiments designed to test using data from two types of collections: microscope slides (from three institutions) and herbarium sheets (from seven institutions). The main contribution of this work is the method, software and ground truth sets created for this cross-validation as they can be reused in testing similar segmentation proposals in the context of digitization of natural history collections. The cross-validation of segmentation methods should be a required step in the integration of such methods into image processing workflows for natural history collections. 

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Parvathy Rajendran

Papers

Text/Image Region Separation for Document Layout Detection of Old Document Images Using Non-linear Diffusion and Level Set☆