Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: background and handwritten annotation. The best model achieves a mean Intersection over Union (IoU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approach
Online and offline handwritten Chinese text recognition (HTCR) has been studied for decades. Early methods adopted oversegmentation-based strategies but suffered from low speed, insufficient accuracy, and high cost of ch…
Handwritten Chinese Text RecognitionSegmentationWeakly-supervised LearningRecognition of Handwritten Japanese Characters Using Ensemble of Convolutional Neural Networks
The Japanese writing system is complex, with three character types of Hiragana, Katakana, and Kanji. Kanji consists of thousands of unique characters, further adding to the complexity of character identification and lite…
Deep Convolutional Network for Handwritten Chinese Character Recognition
In this project we explored the performance of deep convolutional neural network on recognizing handwritten Chinese characters. We ran experiments on a 200-class and a 3755-class dataset using convolutional networks w…
CALText: Contextual Attention Localization for Offline Handwritten Text
Recognition of Arabic-like scripts such as Persian and Urdu is more challenging than Latin-based scripts. This is due to the presence of a two-dimensional structure, context-dependent character shapes, spaces and overlap…
DecoderText Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network
This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inco…
Segmentation