ICFHR2016 Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts

Abstract

This paper presents the results of the Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15 th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript Images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript Images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.

Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

“…The Southeast Asian manuscripts with different scripts and languages provide real challenges for document analysis methods, not only because of the different forms of characters in the script, but also because the writing style of each script (e.g., how to join or separate a character in a text line) differs. It ranges widely from a binarization process [23][24][25], text line segmentation [26,27], and character and text recognition tasks [25,28,29], to the word spotting methods [30].…”

“…We tested and evaluated some well-known standard binarization methods, and some binarization methods that are experimentally promising for historical archive documents, though not specifically for images of palm leaf manuscripts. We also tested the binarization methods from the Document Image Binarization Competition (DIBCO) competition [37,38], for example Howe's method [39] and the ones from the International Conference on Frontiers in Handwriting Recognition (ICFHR) competition (amadi.univ-lr.fr/ICFHR2016_Contest) [25,40].…”

“…NICK method proposes a threshold computation derived from the basic Niblack's method and the Rais method proposes an optimal size of window for the local binarization. [25]. The best method in this competition employs a Fully Convolutional Network (FCN).…”

“…For Khmer manuscripts, one ground truth binarized image is provided for each image, but for Balinese and Sundanese manuscripts, each image has two different ground truth binarized images [17,25]. The study of ground truth variability and subjectivity was reported in the previous work [24].…”

“…A pressure-sensitive tip stylus is used to trace each text stroke by keeping the original size of the stroke width [59]. For the manuscripts from Bali, the binarized ground truth images have been created with a semi-automatic scheme [17,[23][24][25] (Figure 12). The binarized ground truth images for Sundanese manuscripts were manually [22] generated using PixLabeler [60] (Figure 13).…”