A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts

This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, web-based GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.Comment: Oral presentation at International Conference on Document Analysis and Recognition (ICDAR) - 2019. For dataset, pre-trained networks and additional details, visit project page at http://ihdia.iiit.ac.in

AKSALont: Aplikasi transliterasi aksara Lontar Bali dengan model LSTM

This study aims to develop an automatic transliteration application for the Balinese palm leaf manuscripts into the Latin/Roman alphabet. The input for this system is the digital image of the original text from the ancient Balinese palm leaf manuscripts, not from the Balinese script, which is printed using a font on a computer. In this study, a segmentation-free transliteration machine using the LSTM model was implemented. In addition, the implementation of the AKSALont application is carried out for the interactions on a web-based platform using cross-platform interoperability. The experimental results show that the machine can transliterate Balinese characters on the Balinese palm-leaf manuscript images properly with a CER of 19.78 % using 10.475 test data. With a web-based online platform, AKSALont has been able to open wider access for the public to the web-based content with an online platform collection.Penelitian ini bertujuan untuk membangun sebuah aplikasi transliterasi aksara Lontar Bali menuju alfabet Latin/Romawi. Citra aksara Lontar Bali yang menjadi masukan bagi sistem ini adalah citra aksara Lontar Bali dari teks yang tertulis pada citra digital dari naskah kuno asli dari Lontar Bali, bukan dari aksara Bali yang tercetak dengan menggunakan font pada komputer. Mesin transliterasi menggunakan model LSTM sehingga proses transliterasi dapat dilakukan tanpa melalui proses segmentasi glyph. Selain itu, dilakukan perancangan dan implementasi interaksi aplikasi AKSALont pada platform berbasis web menggunakan metode interoperabilitas antar platform. Hasil eksperimen menunjukkan bahwa mesin transliterasi yang dibangun sudah menunjukkan kemampuan untuk melakukan transliterasi aksara Bali pada citra Lontar Bali dengan benar dan memiliki CER 19,78 % pada 10.475 data uji. Aplikasi AKSALont yang berbasis web dengan platform daring telah dapat membuka akses yang lebih meluas bagi masyarakat terhadap konten koleksi Lontar Bali

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

This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community