Handwritten text recognition is a challenging task, especially for Indic scripts that have over 100 unique basic Unicode characters. The existing OCR systems perform well when the test set's distribution is similar to the data used to train them. However, in real-world scenarios, the performance of these systems drastically reduces when the test set's distribution deviates from the training set. This often leads to dissatisfied user experience. To address this issue, a controlled framework for long-term OCR services has been proposed that aims to provide high-quality output at competitive costs by consistently upgrading an existing Handwritten Hindi OCR model three times on the dataset of 15 users. The proposed strategy involves fixing the budget of four users for each iteration. For the first iteration, the model directly trains on the dataset from the first four users. For subsequent iterations, all remaining users write a page each which service providers later analyze to select four new best users based on quality predictions on human-readable words. Selected users then write 23 more pages for upgrading the model using Curriculum Learning (CL) on data available in current and previous iterations. Insights into investigations on CL, user selection and data from unseen writing styles have been provided along with testing of upgraded models on held-out sets of one page each from all 23 users. This work can be used for long-term OCR services in crowd-sourcing scenarios for service providers and end-users alike. Furthermore, it has been observed that most Indic languages derived from Indic script are at risk of vanishing due to lack of efforts to preserve them. In many Indic scripts two or more characters often combine to form conjuncts considerably increasing vocabulary tackled by OCR systems. Several methods offer solutions to Indic handwritten text recognition tasks categorized into segmentation free but lexicon dependent methods; segmentation dependent methods; and sequence-to-sequence i.e., cnn-rnn methods.
- Handwritten text recognition is challenging for Indic scripts with over 100 unique basic Unicode characters.
- Existing OCR systems perform well when the test set's distribution is similar to the data used to train them, but performance drastically reduces in real-world scenarios.
- A controlled framework for long-term OCR services has been proposed to consistently upgrade an existing Handwritten Hindi OCR model three times on the dataset of 15 users.
- The proposed strategy involves fixing the budget of four users for each iteration and using Curriculum Learning (CL) on data available in current and previous iterations.
- Insights into investigations on CL, user selection, and data from unseen writing styles have been provided along with testing of upgraded models on held-out sets of one page each from all 23 users.
- This work can be used for long-term OCR services in crowd-sourcing scenarios for service providers and end-users alike.
- Most Indic languages derived from Indic script are at risk of vanishing due to lack of efforts to preserve them.
- Several methods offer solutions to Indic handwritten text recognition tasks categorized into segmentation free but lexicon dependent methods; segmentation dependent methods; and sequence-to-sequence i.e., cnn-rnn methods.
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Summary: The article talks about how hard it is to recognize handwritten text in some languages like Hindi because they have many unique characters. Existing systems work well only when they are tested on similar data as used during training. A new strategy has been proposed to improve OCR models by upgrading them three times using data from 15 users. This method involves selecting four users at each iteration and using their data to train the model better. The study also provides insights into user selection, data from different writing styles, and testing of upgraded models.
Definitions- Handwritten text recognition: The ability of a computer system to read and understand handwritten text.
- Indic scripts: Writing systems used in South Asia, including Hindi, Bengali, Tamil, etc.
- OCR systems: Optical Character Recognition systems that convert images of printed or handwritten text into machine-readable format.
- Unicode characters: A standard system for encoding characters used in digital communication.
- Curriculum Learning (CL): A machine learning technique that involves gradually increasing the complexity of training data over time.
- Segmentation free methods: OCR techniques that do not require dividing the text into separate units (segments) before recognition.
- Lexicon dependent methods: OCR techniques that use pre-defined dictionaries or word lists to aid recognition.
- Sequence-to
Handwritten Text Recognition: A Challenging Task for Indic Scripts
Handwritten text recognition is a difficult task, especially when it comes to Indic scripts that have over 100 unique basic Unicode characters. The existing OCR systems perform well when the test set's distribution is similar to the data used to train them. However, in real-world scenarios, these systems often fail to deliver satisfactory results when the test set's distribution deviates from the training set. To address this issue, a controlled framework has been proposed by researchers for long-term OCR services that aims to provide high-quality output at competitive costs by consistently upgrading an existing Handwritten Hindi OCR model three times on the dataset of 15 users.
Proposed Strategy
The proposed strategy involves fixing the budget of four users for each iteration. For the first iteration, the model directly trains on the dataset from these four users. For subsequent iterations, all remaining users write a page each which service providers later analyze to select four new best users based on quality predictions on human-readable words. Selected users then write 23 more pages for upgrading the model using Curriculum Learning (CL) on data available in current and previous iterations.
Insights into Investigations
Insight into investigations on CL, user selection and data from unseen writing styles have been provided along with testing of upgraded models on held-out sets of one page each from all 23 users. This work can be used for long-term OCR services in crowd-sourcing scenarios for service providers and end-users alike. Furthermore, it has been observed that most Indic languages derived from Indic script are at risk of vanishing due to lack of efforts to preserve them. In many Indic scripts two or more characters often combine to form conjuncts considerably increasing vocabulary tackled by OCR systems.
Methods Offering Solutions
Several methods offer solutions to Indic handwritten text recognition tasks categorized into segmentation free but lexicon dependent methods; segmentation dependent methods; and sequence-to-sequence i.e., cnn-rnn methods such as Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) etc., which are capable of recognizing complex patterns in handwriting better than traditional approaches like Hidden Markov Models (HMMs).
Conclusion
This research paper provides an effective solution towards improving handwritten text recognition accuracy using curriculum learning techniques while also providing insights into investigations related user selection and unseen writing styles datasets which can be beneficial for both service providers and end customers alike who require accurate results without compromising cost effectiveness or quality standards
