With the rapid advancement of generative models, general-purpose generation has gained increasing attention as a promising approach to unify diverse tasks across modalities within a single system. Despite this progress, existing open-source frameworks often remain fragile and struggle to support complex real-world applications due to the lack of structured workflow planning and execution-level feedback. To address these limitations, we present ComfyMind, a collaborative AI system designed to enable robust and scalable general-purpose generation, built on the ComfyUI platform. ComfyMind introduces two core innovations: Semantic Workflow Interface (SWI) that abstracts low-level node graphs into callable functional modules described in natural language, enabling high-level composition and reducing structural errors; Search Tree Planning mechanism with localized feedback execution, which models generation as a hierarchical decision process and allows adaptive correction at each stage. Together, these components improve the stability and flexibility of complex generative workflows. We evaluate ComfyMind on three public benchmarks: ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and reasoning tasks. Results show that ComfyMind consistently outperforms existing open-source baselines and achieves performance comparable to GPT-Image-1. ComfyMind paves a promising path for the development of open-source general-purpose generative AI systems.

Recognition of handwritten text is a useful technique that can be applied in different applications, such as signature recognition, bank check recognition, etc. However, the off-line handwritten text recognition in an unconstrained situation is still a very challenging task due to the high complexity of text strokes and image background. This paper presents a novel segmented handwritten text recognition technique that ensembles recurrent neural network (RNN) classifiers. Two RNN models are first trained that take advantage of the widely used geometrical feature and the Histogram of Oriented Gradient (HOG) feature, respectively. Given a handwritten word image, the optimal recognition result is then obtained by integrating the two trained RNN models together with a lexicon. Experiments on public datasets show the superior performance of our proposed technique.

Text segmentation provides important clues for the accurate identification of character locations and the analysis of character properties such as shape estimation and texture synthesis. In this paper, we propose a robust text segmentation method that employs Markov Random Field (MRF) and use graph cut algorithms to solve the energy minimization problem. To effectively select accurate seeds to boost the text segmentation performance, stroke feature transform is adopted to robustly identify text seeds and text edges. Background seeds are obtained near the text edges in order to well preserve the text boundaries. The energy functions are defined as an MRF consisting of data energy and smoothness energy which can be efficiently solved by graph cut algorithms. One distinctive property of the proposed technique is that it can identify more distinctive seeds so that only one cut is needed to well separate the text regions from the background, hence much faster than the existing iterative graph cut approach. Experiments on ICDAR 2003 and ICDAR 2011 datasets show that the proposed technique obtains superior performance on both pixel level and atom level segmentation.

This paper presents a new approach for human hair length detection. In contrast to others, the proposed method is able to segment hairs from different views of human heads even with low resolution. Faces are not necessary to be visible in the images, as no face detection is needed in our method. Firstly, it conducts background subtraction to detect foreground objects and then detects human heads with a trained human head detector. Next, histogram analysis is carried out on the detected head region to segment hair region with K-mean clustering. Finally, hair length is determined by performing line scanning on the segmented hair region. The main advantages of the proposed method are able to handle the cases of (1) view variation and (2) low resolution or small human body appearance in the image. It is specially designed for surveillance system. The detected human hair length can be used as an invariant feature to be embedded into a human appearance model which can be employed for human detection, indexing and searching in multi cameras network system. This method aims at the fast processing speed and is able to achieve 7ms for hair length identification in a head patch detected. The experiments show the improvements in terms of speed and accuracy.

Clinical features found in brain CT scan images are widely used in traumatic brain injury (TBI) as indicators for Glasgow Outcome Scale (GOS) prediction. However, due to the lack of automated methods to measure and quantify the CT scan image features, the computerized prediction of GOS in TBI has not been well studied. This paper introduces an automated GOS prediction system for traumatic brain CT images. Different from most existing systems that perform the prognosis based on pre-processed data, our system directly works on brain CT scan images based on the image features. Our system can also be extended to large dataset with easy adaptation. For each new image of a CT scan series, our proposed system first makes use of sparse representation model that predicts the GOS of each CT image slice using Gabor features. Logistic regression, which integrates the GOS of each CT scan slice with a pre-trained model, is then applied to estimate the GOS score for the new case which contains multiple CT slices. Evaluation of the system has shown promising results in prediction of GOS of traumatic brain injury cases.

Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional cooccurrence histogram of oriented gradient (ConvCoHOG), which is more robust and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG). In the proposed technique, a more informative feature is constructed by exhaustively extracting features from every possible image patches within character images. Experiments on two public datasets including the ICDAr 2003 Robust Reading character dataset and the Street View Text (SVT) dataset, show that our proposed character recognition technique obtains superior performance compared with state-of-the-art techniques.

The segmentation of scene text from the image background has shown great importance in scene text recognition. In this paper, we propose a multi-level MSER technology that identifies the best-quality text candidates from a set of stable regions that are extracted from different color channel images. In order to identify the best-quality text candidates, a segmentation score is defined which exploits four measures to evaluate the text probability of each stable region including: 1) Stroke width that measures the small stroke width variation of the text, 2) Boundary curvature that measures the smoothness of the stable region boundary, 3) Character confidence that measures the likelihood of a stable region being text based on a pre-trained support vector classifier, 4) Color constancy that measures the global color consistency of each selected text candidate. Finally, the MSERs with the best segmentation score from each channel are combined to form the final segmentation. The proposed method is evaluated on the ICDAR2003 and SVT datasets and experiments show that it outperforms both popular document image binarization methods and state of the art scene text segmentation methods.

Document Image Binarization is a technique to segment text out from the background region of a document image, which is a challenging task due to high intensity variations of the document foreground and background. Recently, a series of document image binarization contests (DIBCOs) had been held that have drawn great research interest in this area. Several document binarization techniques have been proposed and achieve great performance on the contest datasets. However, those proposed techniques may not perform well on all kinds of degraded document images because it is difficult to design a classification method that correctly models the non-uniform degraded document background and text foreground simultaneously. In this paper, we propose a self learning classification framework that combines binary outputs of different binarization methods. The proposed framework makes used of the sparse representation to re-classify the document pixels and produces a better binary results. The experimental results on the recent DIBCO contests show the great performance and robustness of our proposed framework on different kinds of degraded document images.

Scene text recognition is a fundamental step in End-to-End applications where traditional optical character recognition (OCR) systems often fail to produce satisfactory results. This paper proposes a technique that uses co-occurrence histogram of oriented gradients (Co-HOG) to recognize the text in scenes. Compared with histogram of oriented gradients (HOG), Co-HOG is a more powerful tool that captures spatial distribution of neighboring orientation pairs instead of just a single gradient orientation. At the same time, it is more efficient compared with HOG and therefore more suitable for real-time applications. The proposed scene text recognition technique is evaluated on ICDAR2003 character dataset and Street View Text (SVT) dataset. Experiments show that the Co-HOG based technique clearly outperforms state-of-the-art techniques that use HOG, Scale Invariant Feature Transform (SIFT), and Maximally Stable Extremal Regions (MSER).

Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by blood vessel rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Canny's edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques.

Musical staff line detection and removal techniques detect the staff positions in musical documents and segment musical score from musical documents by removing those staff lines. It is an important preprocessing step for ensuing the Optical Music Recognition tasks. This paper proposes an effective staff line detection and removal method that makes use of the global information of the musical document and models the staff line shape. It first estimates the staff height and space, and then models the shape of the staff line by examining the orientation of the staff pixels. At last the estimated model is used to find out the location of staff lines and hence to remove those detected staff lines. The proposed technique is simple, robust, and involves few parameters. It has been tested on the dataset of the recent staff removal competition held under the International Conference of Document Analysis and Recognition(ICDAR) 2011. Experimental results show the effectiveness and robustness of our proposed technique on musical documents with various types of deformations.

In this paper, we propose a method based on gradient vector flow for video character segmentation. By formulating character segmentation as a minimum cost path finding problem, the proposed method allows curved segmentation paths and thus it is able to segment overlapping characters and touching characters due to low contrast and complex background. Gradient vector flow is used in a new way to identify candidate cut pixels. A two-pass path finding algorithm is then applied where the forward direction helps to locate potential cuts and the backward direction serves to remove the false cuts, i.e. those that go through the characters, while retaining the true cuts. Experimental results show that the proposed method outperforms an existing method on multi-oriented English and Chinese video text lines. The proposed method also helps to improve binarization results, which lead to a better character recognition rate.

This paper presents a new method based on Fourier and moments features to extract words and characters from a video text line in any direction for recognition. Unlike existing methods which output the entire text line to the ensuing recognition algorithm, the proposed method obtains each extracted character from the text line as input to the recognition algorithm because the background of a single character is relatively simple compared to the text line and words. Max-Min clustering criterion is introduced to obtain text cluster from the extracted Fourier and moments feature set. Union of the text cluster with Canny operation of the input video text line is proposed to obtain missing text candidates. Then a run length criterion is used for extraction of words. From the words, we propose a new idea for extracting characters from the text candidates of each word image based on the fact that the text height difference at the character boundary column is smaller than that at other columns of the word image. We evaluate the method on a large dataset at three levels namely text line, words and characters in terms of recall, precision and f-measure. In addition to this, we show that the recognition result for the extracted character is better than words and lines. Our experimental set up involves 3527 characters including Chinese. The dataset is selected from TRECVID database of 2005 and 2006.

The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute gradient feature matrix of the video text line image. Union of the text cluster with the output of Canny operation of the input video text line is proposed to restore missing text candidates. Then a run length algorithm is applied on the text candidate image for identifying word gaps. We propose a new idea for segmenting characters from the restored word image based on the fact that the text height difference at the character boundary column is smaller than that of the other columns of the word image. We have conducted experiments on a large dataset at two levels (word and character level) in terms of recall, precision and f-measure. Our experimental setup involves 3527 characters of English and Chinese, and this dataset is selected from TRECVID database of 2005 and 2006.

Document image binarization has been studied for decades, and many practical binarization techniques have been proposed for different kinds of document images. However, many state-of-the-art methods are particularly suitable for the document images that suffer from certain specific type of image degradation or have certain specific type of image characteristics. In this paper, we propose a classification framework to combine different thresholding methods and produce better performance for document image binarization. Given the binarization results of some reported methods, the proposed framework divides the document image pixels into three sets, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then applied to iteratively classify those uncertain pixels into foreground and background, based on the pre-selected froeground and background sets. Extensive experiments over different datasets including the Document Image Binarization Contest(DIBCO)2009 and Handwritten Document Image Binarization Competition(H-DIBCO)2010 show that our proposed framework outperforms most state-of-the-art methods significantly.

Many digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image deblurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval.

This paper presents a new document image binarization technique that segments the text from badly degraded historical document images. The proposed technique makes use of the image contrast that is defined by the local image maximum and minimum. Compared with the image gradient, the image contrast evaluated by the local maximum and minimum has a nice property that it is more tolerant to the uneven illumination and other types of document degradation such as smear. Given a historical document image, the proposed technique first constructs a contrast image and then detects the high contrast image pixels which usually lie around the text stroke boundary. The document text is then segmented by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window. The proposed technique has been tested over the dataset that is used in the recent Document Image Binarization Contest (DIBCO) 2009. Experiments show its superior performance.

Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a self-training learning framework for document image binarization. Based on reported binarization methods, the proposed framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories. Finally, the uncertain pixels are classified using the learned pixel classifier. Extensive experiments have been conducted over the dataset that is used in the recent Document Image Binarization Contest (DIBCO) 2009. Experimental results show that our proposed framework significantly improves the performance of reported document image binarization methods.