Abstract

This paper presents a diverse compilation of Indic offline handwritten documents. Our dataset comprises 91K handwritten document images captured through unconstrained camera across thirteen Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Manipuri, Marathi, Oriya, Punjabi, Tamil, Telugu, and Urdu, contributed by 1,220 writers. This dataset encompasses 2600K words and includes 566,187 unique words featuring diverse content types, such as alphabetic and numeric. Additionally, we establish a high baseline for the proposed dataset, facilitating evaluation, benchmarking and explicitly focusing on word recognition tasks. Our findings indicate that our dataset is an effective training source for enhancing performance on respective datasets.

Abstract

The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively predicting maneuvers before they occur, thereby strengthening rider safety. This capability enables the riders to react to the potential incorrect maneuvers flagged by advanced driver assistance systems (ADAS). We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP. The dataset incorporates a spectrum of traffic conditions and challenging navigational maneuvers on roads with varying lighting conditions. For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks: one state-space model (Mamba2) and two learning-based approaches (SVM and CNN-LSTM). The results indicate that the state-space model outperformed the other methods across the entire dataset, providing a balanced performance across maneuver classes. The SVM-based RIP method showed the second-best performance when using random sampling and SMOTE. However, the CNN-LSTM method underperformed, primarily due to class imbalance issues, particularly struggling with minority classes. This paper details the proposed RAAD dataset and provides a summary of the submissions for the RIP 2024 competition.

Abstract

The problem of determining geolocation through visual inputs, known as Visual Place Recognition (VPR), has attracted significant attention in recent years owing to its potential applications in autonomous self-driving systems. The rising interest in these applications poses unique challenges, particularly the necessity for datasets encompassing unstructured environmental conditions to facilitate the development of robust VPR methods. In this paper, we address the VPR challenges by proposing an Indian driving VPR dataset that caters to the semantic diversity of unstructured driving environments like occlusions due to dynamic environments, variations in traffic density, viewpoint variability, and variability in lighting conditions. In unstructured driving environments, GPS signals are unreliable often affecting the vehicle to accurately determine location. To address this challenge, we develop an interactive image-to-image tagging annotation tool to annotate large datasets with ground truth annotations for VPR training. Evaluation of the state-of-the-art methods on our dataset shows a significant performance drop of up to 15%, defeating a large number of standard VPR datasets. We also provide an exhaustive quantitative and qualitative experimental analysis of frontal-view, multi-view, and sequence-matching methods. We believe that our dataset will open new challenges for the VPR research community to build robust models.

Abstract

With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts the binary cross/no-cross intention with a fusion of visual and motion features. However, there has been no effort so far to hedge such predictions with humanunderstandable reasons. We address this issue by introducing a novel problem setting of exploring the intuitive reasoning behind a pedestrian’s intent. In particular, we show that predicting the ‘WHY’ can be very useful in understanding the ‘WHAT’. To this end, we propose a novel, reasonenriched PIE++ dataset consisting of multi-label textual explanations/reasons for pedestrian intent. We also introduce a novel multi-task learning framework called MINDREAD, which leverages a cross-modal representation learning framework for predicting pedestrian intent as well as the reason behind the intent. Our comprehensive experiments show significant improvement of 5.6% and 7% in accuracy and F1-score for the task of intent prediction on the PIE++ dataset using MINDREAD. We also achieved a 4.4% improvement in accuracy on a commonly used JAAD dataset. Extensive evaluation using quantitative/qualitative metrics and user studies shows the effectiveness of our approach.

Abstract

Prior works have addressed the problem of driver intention prediction (DIP) by identifying maneuvers after their onset. On the other hand, early anticipation is equally important in scenarios that demand a preemptive response before a maneuver begins. However, there is no prior work aimed at addressing the problem of driver action anticipation before the onset of the maneuver, limiting the ability of the advanced driver assistance system (ADAS) for early maneuver anticipation. In this work, we introduce Anticipating Driving Maneuvers (ADM), a new task that enables driver action anticipation before the onset of the maneuver. To initiate research in ADM task, we curate Driving Action Anticipation Dataset, DAAD, that is multi-view: in- and out-cabin views in dense and heterogeneous scenarios, and multimodal: egocentric view and gaze information. The dataset captures sequences both before the initiation and during the execution of a maneuver. During dataset collection, we also ensure to capture wide diversity in traffic scenarios, weather and illumination, and driveway conditions. Next, we propose a strong baseline based on a transformer architecture to effectively model multiple views and modalities over longer video lengths. We benchmark the existing DIP methods on DAAD and related datasets. Finally, we perform an ablation study showing the effectiveness of multiple views and modalities in maneuver anticipation

Abstract

This paper presents the competition report on Reading Documents through Aria Glasses (ICDAR 2024 RDTAG) held at the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). From a mixed reality perspective, understanding the text in the world is of paramount importance. However, all day long, always on, machine perception devices like Aria Glasses pose a unique primary challenge of lower resolution due to their power and sensor constraints. Moreover, diverse everyday scenes like variations in the lighting conditions and reading positions further complicate the reading tasks. To address this, we propose a new dataset and a challenge. Specifically, we propose three novel tasks: Isolated Word Recognition in Low Resolution (Task A), Prediction of Reading Order (Task B), and Page Level Recognition (Task C). We provide new training and test sets consisting of document images captured by Aria Glasses while reading diverse documents in English under various everyday scenarios. Our aim is to engage researchers with prior experience in English language OCR, and to establish benchmarks contributing to the academic literature in this field. A total of thirty-three different teams from around the world registered for this competition, and twelve teams submitted their results along with algorithm details. The winning team, SRCB, achieved a 97.23% Character Recognition Rate (CRR) and a 90.45% Word Recognition Rate (WRR) for Task A: Isolated Word Recognition in Low Resolution. Team Gang-of-N won Task B: Prediction of Reading Order with a BLEU score of 0.0939. Team SRCB also won Task C: Page Level Recognition and Reading with a 77.44% average Page Level Character Recognition Rate (PCRR) and a 50.55% average Page Level Word Recognition Rate (PWRR).

Abstract

This paper presents a competition report on the Recognition and Visual Question Answer on Handwritten Documents towards deeper understanding of handwritten multilingual documents (ICDAR 2024-HWD) held at the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). Documents are in English or Indian Languages. Earlier editions related to recognition of Indian handwriting were held in conjunction with ICFHR 2022 and ICDAR 2023. A related DocVQA task was held in DAS 2020. This edition proposes three main tasks: Isolated Word Recognition (Task A), Page Level Recognition and Reading (Task B), and Visual Question Answers on Handwritten Documents (Task A). While Task A was already part of our previous competitions, we bring in new data as part of this edition. Task B and Task C are novel additions for this year. By attracting researchers with experience in printed and handwritten documents, we aim to establish benchmarks that significantly contribute to the academic literature in this field. A total of thirty-two teams around the world registered for this competition. Among them, only ten teams submitted their results along with algorithm details. The winning team, TSNUK, achieved an average 98.00% Character Recognition Rate (CRR) and 94.26% Word Recognition Rate (WRR) across four languages for Task A: Isolated Word Recognition. IndependentOCR excelled in Task B: Page Level Recognition and Reading, with 76.32% average Page Level Character Recognition Rate (PCRR) and 62.57% average Page Level Word Recognition Rate. The team PA_VCG won Task C: Visual Question Answering on Handwritten Documents with a 0.643 ANLS score.

Abstract

Extensive research and the development of benchmark datasets have primarily focused on Scene Text Recognition (STR) in Latin languages. However, the scenario differs for Indian languages, where the complexities in syntax and semantics have posed many challenges, resulting in limited datasets and comparatively less research in this domain. Overcoming these challenges is crucial for advancing scene text recognition in Indian languages. Although a few works have touched upon this issue, they are constrained in the size and scale of the data as far as we know. To bridge this gap, this paper introduces a large scale, diverse dataset, named as IIIT-IndicSTR-Word for Indic scene text. Comprising a total of 250K word level images in ten different languages—Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu, these images are extracted from roadside scenes captured by a GoPro camera. The dataset encompasses a wide array of realistic adversarial conditions, including blur, changes in illumination, occlusion, non-iconic texts, low resolution, and perspective text. We establish a baseline for the proposed dataset, facilitating evaluation and benchmarking with a specific focus on STR tasks. Our findings indicate that our dataset is a practical training source to enhance performance on respective datasets

Abstract

The practical applications of Handwritten Text Recognition (HTR) have flourished with many successful commercial APIs, solutions, and diverse use cases. Despite the availability of numerous industrial solutions, academic research in HTR, particularly for English, has been hindered by the scarcity of publicly accessible data. To bridge this gap, this paper introduces IIIT-HW-English-Word, a large and diverse collection of offline handwritten English documents. This dataset comprises unconstrained camera-captured images featuring 20,800 handwritten documents crafted by 1,215 writers. Within this dataset, covering 757,830 words, we identify 174,701 unique words encompassing a variety of content types, such as alphabetic, numeric, and stop-words. We also establish a baseline for the proposed dataset, facilitating evaluation and benchmarking, explicitly focusing on word recognition tasks. Our findings suggest that our dataset can effectively serve as a training source to enhance performance on respective datasets

Abstract

In the current digital era, about 80% of the digital data which is being generated is unstructured and unlabeled natural language text. In the development cycle of information retrieval and text mining applications, text representation is the most fundamental and critical step, as its effectiveness directly impacts the application’s performance. The existing traditional text representation frameworks are mostly frequency distribution-based. In this work, we explored the spatial distribution of word embeddings and proposed two text representational models. The experimental demonstrated that proposed models perform consistently better at text mining tasks compared to baseline methods.